{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":58389,"title":"Minimum number of crossings in a complete graph","description":"This problem is related to problem 58384.\r\nA complete graph may be drawn in different ways, such that the number of line crossings differs between drawings.\r\nTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\r\nNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\r\nGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\r\nExample:\r\nn = 6;\r\nb = 3","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 242.898px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 121.449px; transform-origin: 406.996px 121.449px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is related to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/58384-minimum-number-of-crossings-in-a-complete-bipartite-graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 58384\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecomplete graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8807px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 20.4403px; transform-origin: 403.999px 20.4403px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003en = 6;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eb = 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b = your_fcn_name(n)\r\n b = n;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext,'regexp')))\r\nassert(isempty(strfind(filetext,'assign')))\r\nassert(isempty(strfind(filetext,'eval')))\r\nassert(isempty(strfind(filetext,'echo')))\r\nassert(isempty(strfind(filetext,'!')))\r\n\r\n%%\r\nn = 6;\r\nassert(isequal(your_fcn_name(n),3))\r\n\r\n%%\r\nn = 12;\r\nassert(isequal(your_fcn_name(n),150))\r\n\r\n%%\r\nn = 35;\r\nassert(isequal(your_fcn_name(n),18496))\r\n\r\n%%\r\nn = 84;\r\nassert(isequal(your_fcn_name(n),706020))\r\n\r\n%%\r\nn = 99;\r\nassert(isequal(your_fcn_name(n),1382976))\r\n\r\n%%\r\nn = 200;\r\nassert(isequal(your_fcn_name(n),24012450))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-06-02T18:43:47.000Z","updated_at":"2026-03-03T22:13:51.000Z","published_at":"2023-06-02T18:43:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/58384-minimum-number-of-crossings-in-a-complete-bipartite-graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 58384\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecomplete graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[n = 6;\\nb = 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58384,"title":"Minimum number of crossings in a complete bipartite graph","description":"This problem is related to problem 58389.\r\nA complete bipartite graph may be drawn in different ways, such that the number of line crossings differs between drawings.\r\nTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\r\nNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\r\nGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\r\nExample:\r\nm = 3;\r\nn = 4;\r\nb = 2","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 284.347px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 142.173px; transform-origin: 406.996px 142.173px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is related to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/58389\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 58389\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_bipartite_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecomplete bipartite graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.321px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 30.6534px; transform-origin: 403.999px 30.6605px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em = 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003en = 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eb = 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b = your_fcn_name(m,n)\r\n b = m * n;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext,'regexp')))\r\nassert(isempty(strfind(filetext,'assign')))\r\nassert(isempty(strfind(filetext,'eval')))\r\nassert(isempty(strfind(filetext,'echo')))\r\nassert(isempty(strfind(filetext,'!')))\r\n\r\n%%\r\n[m,n] = deal(3,4)\r\nassert(isequal(your_fcn_name(m,n),2))\r\n\r\n%%\r\n[m,n] = deal(4,3)\r\nassert(isequal(your_fcn_name(m,n),2))\r\n\r\n%%\r\n[m,n] = deal(12,15)\r\nassert(isequal(your_fcn_name(m,n),1470))\r\n\r\n%%\r\n[m,n] = deal(43,19)\r\nassert(isequal(your_fcn_name(m,n),35721))\r\n\r\n%%\r\n[m,n] = deal(108,8)\r\nassert(isequal(your_fcn_name(m,n),34344))\r\n\r\n%%\r\n[m,n] = deal(79,85)\r\nassert(isequal(your_fcn_name(m,n),2683044))\r\n\r\n%%\r\n[m,n] = deal(435,187)\r\nassert(isequal(your_fcn_name(m,n),407272761))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":15521,"edited_by":15521,"edited_at":"2023-06-02T18:45:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-06-02T18:28:39.000Z","updated_at":"2023-06-02T18:45:28.000Z","published_at":"2023-06-02T18:28:39.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/58389\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 58389\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_bipartite_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecomplete bipartite graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[m = 3;\\nn = 4;\\nb = 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1170,"title":"Count the Number of Undirected Cycles in a Graph","description":"Given a symmetric adjacency matrix, determine the number of unique undirected cycles.\r\nFor example, the graph represented by adjacency matrix\r\nA = [\r\n 0 1 1 0 1\r\n 1 0 1 1 0\r\n 1 1 0 1 1\r\n 0 1 1 0 0\r\n 1 0 1 0 0];\r\nhas 6 cycles. They are:\r\n[1 -\u003e 2 -\u003e 3 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 5 -\u003e 1]\r\n[2 -\u003e 3 -\u003e 4 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 1]\r\n[1 -\u003e 2 -\u003e 3 -\u003e 5 -\u003e 1]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 5 -\u003e 1]\r\nThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 399.2px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 199.6px; transform-origin: 407px 199.6px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 217.5px 8px; transform-origin: 217.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a symmetric adjacency matrix, determine the number of unique\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eundirected\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e cycles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the graph represented by adjacency matrix\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 1 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehas 6 cycles. They are:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 112px 8.5px; tab-size: 4; transform-origin: 112px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381px 8px; transform-origin: 381px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nCycles = count_undirected_cycles(A)\r\n nCycles = 0;\r\nend","test_suite":"%%\r\nA = [\r\n 0 1 1 0 1\r\n 1 0 1 1 0\r\n 1 1 0 1 1\r\n 0 1 1 0 0\r\n 1 0 1 0 0];\r\nnCycles = 6;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 1 1 1 0 1 0\r\n 0 0 0 1 1 1 1 1\r\n 1 0 0 0 1 0 1 0\r\n 1 1 0 0 1 0 0 1\r\n 1 1 1 1 0 0 0 1\r\n 0 1 0 0 0 0 0 1\r\n 1 1 1 0 0 0 0 1\r\n 0 1 0 1 1 1 1 0];\r\nnCycles = 136;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 0 0 1 0 1 1\r\n 0 0 1 1 1 1 0 0 1 0\r\n 0 1 0 1 0 1 1 0 0 1\r\n 0 1 1 0 0 0 0 1 0 0\r\n 0 1 0 0 0 0 0 1 1 1\r\n 0 1 1 0 0 0 0 1 1 0\r\n 1 0 1 0 0 0 0 0 0 0\r\n 0 0 0 1 1 1 0 0 0 0\r\n 1 1 0 0 1 1 0 0 0 1\r\n 1 0 1 0 1 0 0 0 1 0];\r\nnCycles = 251;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 0 1 1 0 0 1\r\n 0 0 1 1 0 0 1 1 0 0\r\n 0 1 0 0 0 1 1 1 0 1\r\n 0 1 0 0 1 0 1 0 1 1\r\n 0 0 0 1 0 0 0 0 1 1\r\n 1 0 1 0 0 0 1 1 1 1\r\n 1 1 1 1 0 1 0 0 0 0\r\n 0 1 1 0 0 1 0 0 1 1\r\n 0 0 0 1 1 1 0 1 0 1\r\n 1 0 1 1 1 1 0 1 1 0];\r\nnCycles = 1579;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 1 1 1 1 0 0\r\n 0 0 0 0 0 1 1 1\r\n 1 0 0 0 1 0 1 0\r\n 1 0 0 0 1 1 0 1\r\n 1 0 1 1 0 1 0 1\r\n 1 1 0 1 1 0 0 1\r\n 0 1 1 0 0 0 0 1\r\n 0 1 0 1 1 1 1 0];\r\nnCycles = 147;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":4,"created_by":1057,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2021-12-31T15:55:01.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T13:44:54.000Z","updated_at":"2025-11-22T15:47:53.000Z","published_at":"2013-01-04T13:48:29.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a symmetric adjacency matrix, determine the number of unique\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eundirected\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cycles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the graph represented by adjacency matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [\\n 0 1 1 0 1\\n 1 0 1 1 0\\n 1 1 0 1 1\\n 0 1 1 0 0\\n 1 0 1 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehas 6 cycles. They are:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[1 -\u003e 2 -\u003e 3 -\u003e 1]\\n[1 -\u003e 3 -\u003e 5 -\u003e 1]\\n[2 -\u003e 3 -\u003e 4 -\u003e 2]\\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 1]\\n[1 -\u003e 2 -\u003e 3 -\u003e 5 -\u003e 1]\\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 5 -\u003e 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58618,"title":"Cycle Detection","description":"Detect if a graph has cycles:\r\nInputs\r\nn: the number of vertices (where each vertex corresponds to an integer from 1 to n)\r\nedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\r\nReturn:\r\ntrue if the graph has cycles and false otherwise","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 171px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 85.5px; transform-origin: 407px 85.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.475px 7.5px; transform-origin: 87.475px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDetect if a graph has cycles:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.0917px 7.5px; transform-origin: 19.0917px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInputs\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.85px 7.5px; transform-origin: 256.85px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003en: the number of vertices (where each vertex corresponds to an integer from 1 to n)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 249.033px 7.5px; transform-origin: 249.033px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 22.7917px 7.5px; transform-origin: 22.7917px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 145.608px 7.5px; transform-origin: 145.608px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003etrue if the graph has cycles and false otherwise\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = has_cycle(n, edges)\r\n y = x;\r\nend","test_suite":"%%\r\nn = 5;\r\nedges = [1 3; 2 3; 3 4; 4 5];\r\ny_correct = false;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n\r\n%%\r\nn = 3;\r\nedges = [];\r\ny_correct = false;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n\r\n%%\r\nn = 6;\r\nedges = [1 3; 2 3; 3 4; 4 5; 5 6; 6 3];\r\ny_correct = true;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2456395,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-18T11:27:52.000Z","updated_at":"2023-07-18T11:27:52.000Z","published_at":"2023-07-18T11:27:52.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetect if a graph has cycles:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInputs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en: the number of vertices (where each vertex corresponds to an integer from 1 to n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003etrue if the graph has cycles and false otherwise\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":475,"title":"Is this group simply connected?","description":"Given connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\r\n\r\nExample 1:\r\n \r\n Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\r\n \r\nThe three nodes of this graph are fully connected, since this graph could be drawn like so:\r\n\r\n 3--8--9\r\n\r\nExample 2:\r\n\r\n Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\r\n\r\nThis graph could be drawn like so:\r\n\r\n 1--2 5--6\r\n | |\r\n 4--3\r\n\r\nThere are two distinct subgraphs.","description_html":"\u003cp\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\u003c/pre\u003e\u003cp\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 3--8--9\u003c/pre\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\u003c/pre\u003e\u003cp\u003eThis graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 1--2 5--6\r\n | |\r\n 4--3\u003c/pre\u003e\u003cp\u003eThere are two distinct subgraphs.\u003c/p\u003e","function_template":"function tf = isConnected(node_pairs)\r\n tf = true;\r\nend","test_suite":"%%\r\n\r\nnode_pairs = [8 9; 8 3];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6 ];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6;\r\n 6 2 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 2 100];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 50 100];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 4 17 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":2,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":36,"test_suite_updated_at":"2012-03-09T22:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-09T22:05:26.000Z","updated_at":"2026-03-03T23:05:27.000Z","published_at":"2012-03-12T16:23:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 8 9\\n 8 3 ]\\n Output tf is true]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 3--8--9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 1 2 \\n 2 3\\n 1 4\\n 3 4\\n 5 6 ]\\n Output tf is false]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1--2 5--6\\n | |\\n 4--3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are two distinct subgraphs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61156,"title":"How Long is the Border Between Unitopia and Zerostan?","description":"Two countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\r\nYou are the surveyor contracted to resolve this problem once and for all.\r\nThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\r\nThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\r\nIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\r\nExample 1\r\nSingle cell surrounded by zeros:\r\nInput:\r\n [0 0 0\r\n 0 1 0\r\n 0 0 0]\r\nOutput: 4\r\nThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\r\nExample 2\r\n\r\nMatrix with other values:\r\nInput:\r\n [0 0 0 0\r\n 1 1 0 0\r\n 2 2 2 2]\r\nOutput: 3\r\nOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are ignored.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1008.33px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 333.5px 504.167px; transform-origin: 333.5px 504.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTwo countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou are the surveyor contracted to resolve this problem once and for all.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSingle cell surrounded by zeros:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e [0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 0 1 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 0 0 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eOutput: 4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 224.667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 112.333px; text-align: left; transform-origin: 309.5px 112.333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMatrix with other values:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e [0 0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 1 1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 2 2 2 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eOutput: 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are ignored.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function borderLength = calculateBorder(map)\r\n borderLength = 0;\r\nend","test_suite":"%% Test 1: Single cell surrounded by zeros\r\nmap = [0 0 0;\r\n 0 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 4))\r\n\r\n%% Test 2: Two adjacent cells\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 3: Matrix with other values\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 2 2 2];\r\nassert(isequal(calculateBorder(map), 3))\r\n\r\n%% Test 4: No border - only 1s\r\nmap = [1 1 1;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 5: No border - only 0s\r\nmap = [0 0 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 6: No border - no 1s or 0s\r\nmap = [2 3 4;\r\n 5 6 7];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 7: Single element - 1\r\nmap = 1;\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 8: Single element - 0\r\nmap = 0;\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 9: L-shaped region of 1s\r\nmap = [0 0 0 0;\r\n 0 1 1 0;\r\n 0 0 1 0;\r\n 0 0 1 1];\r\nassert(isequal(calculateBorder(map), 9))\r\n\r\n%% Test 10: Large block of 1s surrounded by 0s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 1 1 1 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 10))\r\n\r\n%% Test 11: T-shaped region of 1s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 0 1 0 0;\r\n 0 0 1 0 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 12))\r\n\r\n%% Test 12: Rectangular block of 1s\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 1 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 6))\r\n\r\n%% Test 13: Horizontal bar of 1s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 14: C-shaped region\r\nmap = [1 1 1;\r\n 1 0 0;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 15: Nested rectangles\r\nmap = [1 1 1 1;\r\n 1 0 0 1;\r\n 1 0 0 1;\r\n 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 16: U-shaped region of 1s\r\nmap = [1 0 1;\r\n 1 0 1;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 17: Mixed values with connected regions\r\nmap = [0 0 2 2;\r\n 0 1 1 2;\r\n 0 0 1 2];\r\nassert(isequal(calculateBorder(map), 4))\r\n\r\n%% Test 18: Spiral pattern\r\nmap = [1 1 1 1;\r\n 0 0 0 1;\r\n 1 1 0 1;\r\n 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 9))\r\n\r\n%% Test 19: Step pattern\r\nmap = [0 0 0 0;\r\n 1 1 0 0;\r\n 1 1 1 0;\r\n 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 20: Large hollow square\r\nmap = [1 1 1 1 1;\r\n 1 0 0 0 1;\r\n 1 0 0 0 1;\r\n 1 0 0 0 1;\r\n 1 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 12))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":7,"edited_by":7,"edited_at":"2026-01-08T17:18:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-08T17:06:36.000Z","updated_at":"2026-03-06T19:37:51.000Z","published_at":"2026-01-08T17:18:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are the surveyor contracted to resolve this problem once and for all.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSingle cell surrounded by zeros:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e [0 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 0 1 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput: 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"219\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"300\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMatrix with other values:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e [0 0 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 1 1 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 2 2 2 2]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput: 3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2461,"title":"Graph Algorithms - 2 : Chromatic Number","description":"Chromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\r\n","description_html":"\u003cp\u003eChromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\u003c/p\u003e","function_template":"function y = gColor(x)\r\n\r\nend","test_suite":"%%\r\nx = [0 1 1 0; 1 0 1 1 ; 1 1 0 0 ; 0 1 0 0];\r\ny_correct = 3;\r\nassert(isequal(gColor(x),y_correct))\r\n\r\n%%\r\nx = [0 1 0 0 1 0 1;1 0 1 0 0 0 1; 0 1 0 1 0 0 1; 0 0 1 0 0 1 1 ; 1 0 0 0 0 0 1 ; 0 0 0 1 0 0 1; 1 1 1 1 1 1 0];\r\ny_correct = 3;\r\nassert(isequal(gColor(x),y_correct))\r\n\r\n%%\r\nx = fliplr(eye(2));\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\n%%\r\nx = [0 1 1 0 1;1 0 0 1 0;1 0 0 1 0;0 1 1 0 1;1 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\n%%\r\nx = [0 1 1 0 1;1 0 0 1 0;1 0 0 1 0;0 1 1 0 1;1 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\nx = zeros(2); % two vertices, not connected by any edge\r\ny_correct = 1;\r\nassert(isequal(gColor(x),y_correct));","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2014-07-25T14:47:23.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-23T13:48:08.000Z","updated_at":"2014-07-25T14:47:23.000Z","published_at":"2014-07-23T13:48:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2458,"title":"Graph Algorithms - 1 : Length of the largest closed path","description":"An undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\r\n\r\nDefinition of adjacency matrix : \u003chttp://en.wikipedia.org/wiki/Adjacency_matrix\u003e","description_html":"\u003cp\u003eAn undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\u003c/p\u003e\u003cp\u003eDefinition of adjacency matrix : \u003ca href = \"http://en.wikipedia.org/wiki/Adjacency_matrix\"\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/a\u003e\u003c/p\u003e","function_template":"function y = lCycle(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 0 1 0 0 0; 0 0 0 1 0 1; 1 0 0 0 1 0;0 1 0 0 0 1; 0 0 1 0 0 0; 0 1 0 1 0 0];\r\ny_correct = 3;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n%%\r\nx = [0 1 0;1 0 1;0 1 0];\r\ny_correct = 2;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = ones(5).*(~eye(5));\r\ny_correct = 5;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = ones(10).*(~eye(10));\r\ny_correct = 10;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 1 0 0 0 1];\r\nfor i = 1:length(x)\r\n a(i,:) = circshift(x,[0 (i-1)]);\r\nend\r\ny_correct = 6;\r\nassert(isequal(lCycle(a),y_correct))\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2015-01-27T13:28:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-23T10:28:15.000Z","updated_at":"2024-11-02T02:04:14.000Z","published_at":"2014-07-23T10:28:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDefinition of adjacency matrix :\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Adjacency_matrix\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2730,"title":"Graph Algorithms 3: Number of Connected Components","description":"Given an adjacency matrix of a simple undirected graph, find the number of connected components.","description_html":"\u003cp\u003eGiven an adjacency matrix of a simple undirected graph, find the number of connected components.\u003c/p\u003e","function_template":"function y = cComponents(x)\r\n y = x;\r\nend","test_suite":"%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\nmat = [a b; b a];\r\nn = 2;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\na = randi(20);\r\nmat = ones(a) - eye(a);\r\nn = 1;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\nmat = [0 1 0 0 1;\r\n 1 0 1 0 0;\r\n 0 1 0 1 0;\r\n 0 0 1 0 1;\r\n 1 0 0 1 0];\r\nassert(isequal(cComponents(mat),1))\r\n\r\n\r\n%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\nmat = [a b b; b a b; b b a];\r\nn = 3;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\np = floor((randi(20)+3)/3)*3;\r\nmat = [];\r\nfor i= 1:p\r\n c = [repmat(b,1,i-1) a repmat(b,1,p-i)];\r\n mat = [mat;c];\r\nend\r\n\r\nassert(isequal(cComponents(mat),p))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":40,"test_suite_updated_at":"2014-12-07T06:39:46.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-12-06T07:19:29.000Z","updated_at":"2026-03-30T17:12:13.000Z","published_at":"2014-12-06T07:19:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an adjacency matrix of a simple undirected graph, find the number of connected components.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2685,"title":"FloydWarshall","description":"Our task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\r\nExample :\r\n input= [0 1 Inf Inf \r\n Inf 0 2 Inf\r\n Inf Inf 0 3\r\n 4 7 Inf 0]\r\n\r\n output= [0 1 3 6\r\n 9 0 2 5\r\n 7 8 0 3\r\n 4 5 7 0]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 307.9px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 153.95px; transform-origin: 407px 153.95px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.5px 8px; transform-origin: 372.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOur task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.5px 8px; transform-origin: 29.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e :\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 183.9px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 91.95px; transform-origin: 404px 91.95px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e input= [0 1 Inf Inf \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; 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block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4 5 7 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = floydwarshall(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 1 Inf Inf;\r\n Inf 0 2 Inf;\r\n Inf Inf 0 3\r\n 4 7 Inf 0];\r\ny_correct = [0 1 3 6;\r\n 9 0 2 5;\r\n 7 8 0 3;\r\n 4 5 7 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 8 Inf 1;\r\n Inf 0 1 Inf;\r\n 4 Inf 0 Inf;\r\n Inf 2 9 0];\r\ny_correct = [0 3 4 1\r\n 5 0 1 6\r\n 4 7 0 5\r\n 7 2 3 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 3 6 Inf Inf Inf Inf\r\n 3 0 2 1 Inf Inf Inf\r\n 6 2 0 1 4 2 Inf\r\n Inf 1 1 0 2 Inf 4\r\n Inf Inf 4 2 0 2 1\r\n Inf Inf 2 Inf 2 0 1\r\n Inf Inf Inf 4 1 1 0];\r\ny_correct = [0 3 5 4 6 7 7\r\n 3 0 2 1 3 4 4\r\n 5 2 0 1 3 2 3\r\n 4 1 1 0 2 3 3\r\n 6 3 3 2 0 2 1\r\n 7 4 2 3 2 0 1\r\n 7 4 3 3 1 1 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 3 Inf 5;\r\n 2 0 Inf 4;\r\n Inf 1 0 Inf;\r\n Inf Inf 2 0];\r\ny_correct = [0 3 7 5\r\n 2 0 6 4\r\n 3 1 0 5\r\n 5 3 2 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":32478,"edited_by":223089,"edited_at":"2023-01-03T06:19:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2023-01-03T06:19:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-11-23T22:43:29.000Z","updated_at":"2026-03-30T15:58:21.000Z","published_at":"2014-11-23T22:44:41.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOur task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ input= [0 1 Inf Inf \\n Inf 0 2 Inf\\n Inf Inf 0 3\\n 4 7 Inf 0]\\n\\n output= [0 1 3 6\\n 9 0 2 5\\n 7 8 0 3\\n 4 5 7 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61063,"title":"The Bridges of Nedsburg","description":"The fabled city of Nedsburg consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\r\nTherefore, instead of a map, visitors to Nedsburg are given a connectivity matrix (C): a square matrix of 0s and 1s, where C(j,k) = 1 if there is a bridge from island j to island k, and C(j,k) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means C is not necessarily symmetric. That is, it is possible to have C(j,k) = 1 (you can travel directly from island j to island k) and C(k,j) = 0 (you can’t travel directly from island k to island j).\r\nBecause Lord Ned likes to torture visitors with math problems, the Nedsburg transit system has an n-bridge pass which allows you to cross up to n bridges in one journey. Not surprisingly, the pricing scheme gets eye-wateringly expensive as n increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of n that allows them to get from any island to any other island in one journey.\r\nTo prepare for your upcoming trip to Nedsburg to watch the Nedball World Cup, you need to write a function that, given C, returns the minimum n needed so that there is a path from every island to every other island in n steps or fewer. That is, find the smallest n such that every pair of islands is at most n bridges away from each other.\r\nA handy bit of math that might help is that the value of represents the number of paths of exactly m steps from island j to island k. (Here, is the matrix power; that is, , using matrix multiplication.) Therefore, if , then there is no way to get from j to k in exactly m steps. If , there is at least one path (of m steps) from j to k. Therefore, one way to solve the problem is to find the smallest n such that, for every j,k pair, there is some m ≤ n such that .\r\n\r\nExample\r\nConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\r\n\r\n\r\nThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\r\nC = [0 1 1;0 0 1;1 0 0];\r\nC^2\r\nans =\r\n1 0 1\r\n1 0 0\r\n0 1 1\r\nThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, n = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\r\n\r\nAssumptions\r\nWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite n exists for the given connectivity matrix C. This also means the problem is trivial for one or two islands, so your solution only needs to work when C is at least 3-by-3.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1998.43px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 999.213px; transform-origin: 408px 999.213px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe fabled city of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 42px; text-align: left; transform-origin: 385px 42px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTherefore, instead of a map, visitors to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are given a connectivity matrix (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e): a square matrix of 0s and 1s, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 1 if there is a bridge from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is not necessarily symmetric. That is, it is possible to have \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 1 (you can travel directly from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 0 (you can’t travel directly from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 52.5px; text-align: left; transform-origin: 385px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBecause Lord Ned likes to torture visitors with math problems, the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e transit system has an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-bridge pass which allows you to cross up to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bridges in one journey. Not surprisingly, the pricing scheme gets eye-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewateringly\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e expensive as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that allows them to get from any island to any other island in one journey.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo prepare for your upcoming trip to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to watch the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, you need to write a function that, given \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, returns the minimum \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e needed so that there is a path from every island to every other island in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps or fewer. That is, find the smallest \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that every pair of islands is at most \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bridges away from each other.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 128.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 64.1px; text-align: left; transform-origin: 385px 64.1px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA handy bit of math that might help is that the value of \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"53\" height=\"21\" style=\"vertical-align: baseline;width: 53px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e represents the number of paths of exactly \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (Here, \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"22\" height=\"21\" style=\"vertical-align: baseline;width: 22px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the matrix power; that is, \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"91\" height=\"18\" style=\"vertical-align: baseline;width: 91px;height: 18px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, using matrix multiplication.) Therefore, if \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, then there is no way to get from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in exactly \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps. If \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, there is at least one path (of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps) from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Therefore, one way to solve the problem is to find the smallest \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that, for every \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pair, there is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003esome\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ≤ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 366.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 183.4px; text-align: left; transform-origin: 385px 183.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"704\" height=\"361\" style=\"vertical-align: baseline;width: 704px;height: 361px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.625px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405px 61.3125px; transform-origin: 405px 61.3125px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eC = [0 1 1;0 0 1;1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eC^2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 676.8px; font-family: 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\" alt=\"The matrices C and C^2 are shown in two different colors. A grid representing all the possible pairs of nodes has entries in each location showing the minimum number of steps, and the associated path, needed to travel between each pair. (The diagonal entries are blank, as these would represent traveling to the same node/island.) The four non-zero elements of C correspond to four pairs of islands that are connected with a single-step journey. The remaining two pairs (2 to 1 and 3 to 2) are connected with two-step journeys.\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAssumptions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e exists for the given connectivity matrix \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. This also means the problem is trivial for one or two islands, so your solution only needs to work when \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is at least 3-by-3.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = numbridges(C)\r\nn = sum(C==1,\"all\");\r\nend","test_suite":"%% Test case #1: m = 3\r\nC = zeros(3);\r\nidx = [3;4;8];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,2))\r\n\r\n%% Test case #2: m = 3\r\nC = zeros(3);\r\nidx = [3;4;7;8];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,2))\r\n\r\n%% Test case #3: m = 4\r\nC = zeros(4);\r\nidx = [2;7;8;10;13];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #4: m = 4\r\nC = zeros(4);\r\nidx = [4;5;7;8;9;14];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #5: m = 5\r\nC = zeros(5);\r\nidx = [5;8;11;12;14;18;22;23];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #6: m = 5\r\nC = zeros(5);\r\nidx = [2;5;8;14;20;21;24];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #7: m = 6\r\nC = zeros(6);\r\nidx = [3;4;7;11;18;23;24;26;30;32];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #8: m = 6\r\nC = zeros(6);\r\nidx = [3;11;12;13;18;19;28;32];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #9: m = 7\r\nC = zeros(7);\r\nidx = [3;4;6;8;13;14;20;21;23;31;36;47];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,6))\r\n\r\n%% Test case #10: m = 7\r\nC = zeros(7);\r\nidx = [4;11;13;14;19;22;23;26;28;30;34;35;37;38;45];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #11: m = 8\r\nC = zeros(8);\r\nidx = [2;4;5;6;8;12;13;17;27;39;44;48;54;58;60;62];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #12: m = 8\r\nC = zeros(8);\r\nidx = [3;9;12;20;24;29;30;31;33;44;48;50;53;54;58];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #13: m = 9\r\nC = zeros(9);\r\nidx = [8;9;10;14;15;22;25;26;29;33;36;42;44;47;48;50;53;54;55;67;80];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #14: m = 9\r\nC = zeros(9);\r\nidx = [8;10;22;32;37;40;43;45;47;53;56;57;62;64;69;70;73;77;79];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,7))\r\n\r\n%% Test case #15: m = 10\r\nC = zeros(10);\r\nidx = [2;5;8;13;16;20;24;27;28;36;43;49;53;62;71;75;77;83;86;87;95];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #16: m = 10\r\nC = zeros(10);\r\nidx = [4;9;14;21;22;35;37;38;44;47;50;51;53;55;59;61;63;66;69;76;77;84;85;86;90;97];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2025-11-10T02:22:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":87,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-04T20:34:09.000Z","updated_at":"2026-02-13T15:41:43.000Z","published_at":"2025-11-10T02:22:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe fabled city of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, instead of a map, visitors to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are given a connectivity matrix (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e): a square matrix of 0s and 1s, where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 1 if there is a bridge from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is not necessarily symmetric. That is, it is possible to have \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 1 (you can travel directly from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 0 (you can’t travel directly from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBecause Lord Ned likes to torture visitors with math problems, the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e transit system has an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-bridge pass which allows you to cross up to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bridges in one journey. Not surprisingly, the pricing scheme gets eye-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewateringly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e expensive as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that allows them to get from any island to any other island in one journey.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo prepare for your upcoming trip to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to watch the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, you need to write a function that, given \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, returns the minimum \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e needed so that there is a path from every island to every other island in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps or fewer. That is, find the smallest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that every pair of islands is at most \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bridges away from each other.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA handy bit of math that might help is that the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"53\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e represents the number of paths of exactly \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. (Here, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"22\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the matrix power; that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"18\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"91\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, using matrix multiplication.) Therefore, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, then there is no way to get from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in exactly \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps. If \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, there is at least one path (of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps) from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Therefore, one way to solve the problem is to find the smallest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that, for every \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e pair, there is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esome\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ≤ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"361\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"704\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = [0 1 1;0 0 1;1 0 0];\\nC^2\\nans =\\n1 0 1\\n1 0 0\\n0 1 1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"671\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"708\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"The matrices C and C^2 are shown in two different colors. A grid representing all the possible pairs of nodes has entries in each location showing the minimum number of steps, and the associated path, needed to travel between each pair. (The diagonal entries are blank, as these would represent traveling to the same node/island.) The four non-zero elements of C correspond to four pairs of islands that are connected with a single-step journey. The remaining two pairs (2 to 1 and 3 to 2) are connected with two-step journeys.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId7\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAssumptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e exists for the given connectivity matrix \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This also means the problem is trivial for one or two islands, so your solution only needs to work when \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is at least 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connected components of a graph","description":"Adjacency matrix of an undirected graph is given. Return the number of connected components in the graph.","description_html":"\u003cp\u003eAdjacency matrix of an undirected graph is given. Return the number of connected components in the graph.\u003c/p\u003e","function_template":"function y = conctCom(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx=[0 1 0 0 0; 1 0 1 1 0; 0 1 0 1 1; 0 1 1 0 1; 0 0 1 1 0];\r\ny_correct = 1;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=[0 1 0 0 0; 1 0 0 0 0;0 0 0 1 0;0 0 1 0 1;0 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=[0 1 0 0 0 0 ; \r\n1 0 0 0 0 0; \r\n0 0 0 1 0 0; \r\n0 0 1 0 0 0 ; \r\n0 0 0 0 0 1; \r\n0 0 0 0 1 0];\r\ny_correct = 3;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=1;\r\ny_correct = 1;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":61,"test_suite_updated_at":"2014-06-18T16:25:21.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-18T07:37:21.000Z","updated_at":"2026-03-30T15:21:20.000Z","published_at":"2014-06-18T07:37:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAdjacency matrix of an undirected graph is given. Return the number of connected components in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":458,"title":"Parcel Routing","description":"Given a matrix that represent the distance along highways between major cities numbered 1 to _N_, provide the path and shortest distance from a given city, _from_, to a given city, _to_. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.","description_html":"\u003cp\u003eGiven a matrix that represent the distance along highways between major cities numbered 1 to \u003ci\u003eN\u003c/i\u003e, provide the path and shortest distance from a given city, \u003ci\u003efrom\u003c/i\u003e, to a given city, \u003ci\u003eto\u003c/i\u003e. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.\u003c/p\u003e","function_template":"function [route d] = parcel_route( from, to, graph )\r\n route = -1;\r\n d = -1;\r\nend","test_suite":"%%\r\n[route d] = parcel_route( 1, 5, zeros( 5 ) )\r\nassert(route == -1 \u0026\u0026 d == -1);\r\n\r\n%%\r\n[route d] = parcel_route( 1, 2, [0 0.320527862039621 0 0 0;0.320527862039621 0 0 0 0.85044688801616;0 0 0 0 0;0 0 0 0 0;0 0.85044688801616 0 0 0] );\r\nassert( isequal(route,[1 2]) \u0026\u0026 abs( d - 0.320528 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 2, [0 0 0 0 0.648056184801628;0 0 0.168504735306137 0 0;0 0.168504735306137 0 0 0;0 0 0 0 0;0.648056184801628 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 3, 3, [0 0 0 1.07077622171054 0.00497624606093106;0 0 0 0 0;0 0 0 0 0;1.07077622171054 0 0 0 0;0.00497624606093106 0 0 0 0] );\r\nassert( isequal(route,3) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 2, [0 0 0.478447257684744 0.52778921303553 0;0 0 0 0.344727452766697 0;0.478447257684744 0 0 0 0;0.52778921303553 0.344727452766697 0 0 0;0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 1, 4, [0 0 0 0 0;0 0 0 0 0;0 0 0 0 0;0 0 0 0 0;0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 10, 5, [0 0 0 0 0.758920911298127 1.17184862472796 0 0 0 0;0 0 0 0.229051389055984 0 0 0.110344033764499 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0.229051389055984 0 0 0 0 0 0 0 0;0.758920911298127 0 0 0 0 0 0.582786870390757 0 0 0.266149081187709;1.17184862472796 0 0 0 0 0 0.91437757836659 0 0 0.928664694998184;0 0.110344033764499 0 0 0.582786870390757 0.91437757836659 0 0.72845914907191 0.440667818657679 0.0752998054887686;0 0 0 0 0 0 0.72845914907191 0 0 0;0 0 0 0 0 0 0.440667818657679 0 0 0.72584117080215;0 0 0 0 0.266149081187709 0.928664694998184 0.0752998054887686 0 0.72584117080215 0] );\r\nassert( isequal(route,[10 5]) \u0026\u0026 abs( d - 0.266149 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 7, 3, [0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0.348270614748404 0 0.963402246386651 0 0 0 0;0 0 0.348270614748404 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.00647302663148808 0 0;0 0 0.963402246386651 0 0 0 0 1.11338837090812 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.00647302663148808 1.11338837090812 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 7, [0 0 0.529236850857286 0 0 0 1.60144982503606 0 0 0;0 0 0 0 0 0 0.828441215877115 0 0 0;0.529236850857286 0 0 0.0279102825979989 0 0 0 0 0 0.0544746812572747;0 0 0.0279102825979989 0 0 0 0 0 0 0;0 0 0 0 0 1.04094484718858 0 0 0 0;0 0 0 0 1.04094484718858 0 0 0 0 0.124040053577104;1.60144982503606 0.828441215877115 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.37043522751259;0 0 0.0544746812572747 0 0 0.124040053577104 0 0 0.37043522751259 0] );\r\nassert( isequal(route,[4 3 1 7]) \u0026\u0026 abs( d - 2.1586 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 7, [0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.577543761888686 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.124487323633357 0 0 0.679813903514902;0 0.577543761888686 0 0 0 0 0.560623889702786 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0.124487323633357 0.560623889702786 0 0 0 0.250828758360099 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.250828758360099 0 0 0.914651700028183;0 0 0 0.679813903514902 0 0 0 0 0.914651700028183 0] );\r\nassert( isequal(route,[4 7]) \u0026\u0026 abs( d - 0.124487 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 1, 9, [0 0 0.210714289971845 0 0 0.655795786759233 0 0 0.417975370686535 0.0762383289683841;0 0 0 0 0 0 0 0 0 0;0.210714289971845 0 0 0 0 0 0.627639413184948 0.546973506820504 0 0;0 0 0 0 0 0.44290978142888 0 0 0 0;0 0 0 0 0 0 0.494959375382896 0.199417369123429 0.61193318690704 0;0.655795786759233 0 0 0.44290978142888 0 0 0 0 0.295901565877421 0;0 0 0.627639413184948 0 0.494959375382896 0 0 0 0 0;0 0 0.546973506820504 0 0.199417369123429 0 0 0 0 0.882898432991531;0.417975370686535 0 0 0 0.61193318690704 0.295901565877421 0 0 0 0.0999710063468799;0.0762383289683841 0 0 0 0 0 0 0.882898432991531 0.0999710063468799 0] );\r\nassert( isequal(route,[1 10 9]) \u0026\u0026 abs( d - 0.176209 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 12, 3, [0 0.139438875035701 0.112367141305958 0 0 0 0 0 0 0.742115072015769 0 0 0.244537467584915 0 0;0.139438875035701 0 0.135942047224331 0 0 0 0 0 0 0 0.374140881779805 0 0.217860680093506 0.379818098539566 1.17229854239237;0.112367141305958 0.135942047224331 0 0 0 0 0 1.7792137360137 0.350752848520651 0 0 0.284985494377118 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.161446305533344 0 0 0 0.183948436622344 0 0 0;0 0 1.7792137360137 0 0 0 0.161446305533344 0 0 0 0 0 0 0 0;0 0 0.350752848520651 0 0 0 0 0 0 0 0 0 0 0 0.362973369969354;0.742115072015769 0 0 0 0 0 0 0 0 0 0.263865914379949 0 0 0 0;0 0.374140881779805 0 0 0 0 0 0 0 0.263865914379949 0 0 0 0 0;0 0 0.284985494377118 0 0 0 0.183948436622344 0 0 0 0 0 0 0 0;0.244537467584915 0.217860680093506 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.379818098539566 0 0 0 0 0 0 0 0 0 0 0 0 1.863621808387;0 1.17229854239237 0 0 0 0 0 0 0.362973369969354 0 0 0 0 1.863621808387 0] );\r\nassert( isequal(route,[12 3]) \u0026\u0026 abs( d - 0.284985 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 14, 13, [0 0 0 0.0850075668378245 0 0 0 0 0.0463952689981919 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.0850075668378245 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.831785345865625 0 0 0 0 0 0 0 0.300824537605104 0;0 0 0 0 0.831785345865625 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.316676635592728 0 0 0.18465657297998 0 0;0.0463952689981919 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.316676635592728 0 0 0 0 0 0 2.01596808102817;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0.735349103904233 0 0;0 0 0 0 0 0 0 0.18465657297998 0 0 0 0.735349103904233 0 0 0;0 0 0 0 0.300824537605104 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 2.01596808102817 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 8, 8, [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.669844066951192 0 0 1.79425332134151 0 0 0 0;0 0 0 0 0 0.354813543185228 0.350829365088585 0.334017411457367 0 0 0.750194269879854 0 0 0 0.837083783283494;0 0 0 0 0 0 0 0 0 0 0 0.7666462425288 0 0 0;0 0 0 0 0 0 0 0 0 0 0.335432927184154 0.290662441159473 0 0 0;0 0 0.354813543185228 0 0 0 0 0 0 0 0 0.612746104915618 0 1.3702409817804 0;0 0 0.350829365088585 0 0 0 0 0 0 0 0 0 0 0 0;0 0.669844066951192 0.334017411457367 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 1.79425332134151 0.750194269879854 0 0.335432927184154 0 0 0 0 0 0 0 0 0 0;0 0 0 0.7666462425288 0.290662441159473 0.612746104915618 0 0 0 0 0 0 0.600235691901178 0 0;0 0 0 0 0 0 0 0 0 0 0 0.600235691901178 0 0 0;0 0 0 0 0 1.3702409817804 0 0 0 0 0 0 0 0 0.25725306655894;0 0 0.837083783283494 0 0 0 0 0 0 0 0 0 0 0.25725306655894 0] );\r\nassert( isequal(route,8) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 9, 2, [0 0 0 0 0 0 0 0.216161539093326 0 0 0 0 0 0 0;0 0 0 0.154899548332433 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0.195632123891572 0 0.638112022611646 0 0;0 0.154899548332433 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.924920233869358 0 0 0 0 0 0 0.225753938901222;0 0 0 0 0 0 0 0 0 0 0 0.105130198814148 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.216161539093326 0 0 0 0.924920233869358 0 0 0 0.283480661544537 0 0 0 0 0 0;0 0 0 0 0 0 0 0.283480661544537 0 0 0.860820315822094 0 0 0.114189406386242 0;0 0 0 0 0 0 0 0 0 0 0.777006911310097 0.0395282910845656 0.559642782958394 0.0374763085984708 0;0 0 0.195632123891572 0 0 0 0 0 0.860820315822094 0.777006911310097 0 0.107327989339846 0 0 0;0 0 0 0 0 0.105130198814148 0 0 0 0.0395282910845656 0.107327989339846 0 0 0 0;0 0 0.638112022611646 0 0 0 0 0 0 0.559642782958394 0 0 0 0 0;0 0 0 0 0 0 0 0 0.114189406386242 0.0374763085984708 0 0 0 0 0;0 0 0 0 0.225753938901222 0 0 0 0 0 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 6, 8, [0 1.00600776349789 0 0 0.409642943366229 0 0 0 0 0 0 0 0.780942905018081 0.218269812307052 0;1.00600776349789 0 0 0 0 0 0 0 0 0.604439587022491 0 0 0 0 0;0 0 0 2.19497071462911 0 0.384068674620751 0 0 0 0.752596352506117 0.210553220187945 0 0 0 0.101200876472261;0 0 2.19497071462911 0 0 0 0 0 0 0 0 0.0821684991109088 0 0 1.39540244685607;0.409642943366229 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.384068674620751 0 0 0 0.0278385656290563 0 0 0 0 0 0 0 0;0 0 0 0 0 0.0278385656290563 0 0 0 0 0.314664537582249 0 0 0 0.157551652892199;0 0 0 0 0 0 0 0 0 0 1.37753279184511 0 0 0.647734508061038 0.538120114299927;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.604439587022491 0.752596352506117 0 0 0 0 0 0 0 0.38099752988379 0 0 0 0;0 0 0.210553220187945 0 0 0 0.314664537582249 1.37753279184511 0 0.38099752988379 0 0 0 0 0;0 0 0 0.0821684991109088 0 0 0 0 0 0 0 0 0.724941186609613 0 0;0.780942905018081 0 0 0 0 0 0 0 0 0 0 0.724941186609613 0 0 0;0.218269812307052 0 0 0 0 0 0 0.647734508061038 0 0 0 0 0 0 0;0 0 0.101200876472261 1.39540244685607 0 0 0.157551652892199 0.538120114299927 0 0 0 0 0 0 0] );\r\nassert( isequal(route,[6 7 15 8]) \u0026\u0026 abs( d - 0.72351 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 15, 1, [0 0 0 0 0 0 0 0 0 0 0 0.444956179153313 0.694837045312089 0 0 0 1.21296662658388 0 1.56620351515086 0.139996151546743;0 0 0.436509042497808 0 0 0 0 0 0 0.51021617110356 0.382775864014207 0 0 0 0 0 0 0 0.0458660640982067 0;0 0.436509042497808 0 0.142843784706697 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.142843784706697 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.923358421898822 0 0 0 0 0 0 0.535622573665002 0 0 0.0623807988546017 0 0 0 0;0 0 0 0 0.923358421898822 0 0 0 0 0 0 0 0 0 0.0225268450194125 0.789248499651178 0.131644262096824 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0.157622272676696 0.474149476188578 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 1.38990078830045 0 0 0 0 0.691403775437505 0;0 0.51021617110356 0 0 0 0 0 0 0 0 0.597507997139564 0 0 0 0.354205526419423 0 0 0 0 0;0 0.382775864014207 0 0 0 0 0 0 0 0.597507997139564 0 0 0 0.659672915756231 0 0 0 0 0 0;0.444956179153313 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.138835508200668;0.694837045312089 0 0 0 0.535622573665002 0 0.157622272676696 0 0 0 0 0 0 0 0 0.112112626230952 0 0 0 0.0843937952650982;0 0 0 0 0 0 0.474149476188578 0 1.38990078830045 0 0.659672915756231 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.0225268450194125 0 0 0 0.354205526419423 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.0623807988546017 0.789248499651178 0 0 0 0 0 0 0.112112626230952 0 0 0 0 0 0 2.40412068693751;1.21296662658388 0 0 0 0 0.131644262096824 0 0 0 0 0 0 0 0 0 0 0 0 0.332961917093088 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.464569385286 0;1.56620351515086 0.0458660640982067 0 0 0 0 0 0 0.691403775437505 0 0 0 0 0 0 0 0.332961917093088 1.464569385286 0 0;0.139996151546743 0 0 0 0 0 0 0 0 0 0 0.138835508200668 0.0843937952650982 0 0 2.40412068693751 0 0 0 0] );\r\nassert( isequal(route,[15 6 16 13 20 1]) \u0026\u0026 abs( d - 1.14828 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 6, 9, [0 0.366176160541789 0.786653302253499 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.21056171145861;0.366176160541789 0 0 0 0 0 0 0 0 1.00794652016003 0 0 0 0 0 0 0 0 0 0;0.786653302253499 0 0 0 0.00852440175782365 0 0 0 0 0 0.17749671083585 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.104971160045632 0 0.612122585766863 0 0.283798036821908;0 0 0.00852440175782365 0 0 0 0.643083445674635 0 0 0 1.48191061231689 0 0 0 0.200776975353452 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.20656027667801 0 0 0;0 0 0 0 0.643083445674635 0 0 0 0.0293301078099069 0 0 0.0684877514911584 0.244866042619905 0 0 0 0 0.844967783108164 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.0293301078099069 0 0 0 0 0.112122931478046 0 0 0 0.904924565740344 0 0 0 0;0 1.00794652016003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.17749671083585 0 1.48191061231689 0 0 0 0 0 0 0.356911349006201 0 0 0.157837394902406 0 0 0 0 0;0 0 0 0 0 0 0.0684877514911584 0 0.112122931478046 0 0.356911349006201 0 0.682956498987976 0.369934947078526 0 0 0 0 0 0;0 0 0 0 0 0 0.244866042619905 0 0 0 0 0.682956498987976 0 0 0 0 1.43719870645473 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.369934947078526 0 0 0 0 0 0 0 0;0 0 0 0 0.200776975353452 0 0 0 0 0 0.157837394902406 0 0 0 0 0.0962643536575907 0 0 0 0;0 0 0 0.104971160045632 0 0 0 0 0.904924565740344 0 0 0 0 0 0.0962643536575907 0 0 0 0 0.884861315533158;0 0 0 0 0 1.20656027667801 0 0 0 0 0 0 1.43719870645473 0 0 0 0 0.00316254045496533 0.917601066178612 0;0 0 0 0.612122585766863 0 0 0.844967783108164 0 0 0 0 0 0 0 0 0 0.00316254045496533 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.917601066178612 0 0 0;0.21056171145861 0 0 0.283798036821908 0 0 0 0 0 0 0 0 0 0 0 0.884861315533158 0 0 0 0] );\r\nassert( isequal(route,[6 17 18 7 9]) \u0026\u0026 abs( d - 2.08402 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 15, 8, [0 0 0 0 0 0 0 0.444927230771171 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0.0904807891398611 0 0 0.117980802815763 0 0 0 0 0 0 0 0 0 0 0.786791961925432 0 0;0 0 0 0 0 0 0 0 0 0.159132007464481 0.110433064086588 0 0 0 0 0 0 0 0 0.139982926657546;0 0.0904807891398611 0 0 0.388870980511861 0 0 0 0 0 0 0.973527639623757 0 0 0 0 0 0 0 0;0 0 0 0.388870980511861 0 0 0 0.305597627987039 0 0 0 0 0.027077532916115 0 0 0 0 0 0.290796760442986 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.117980802815763 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.27266472620458 0.665594866955372 0 0.626568354787985;0.444927230771171 0 0 0 0.305597627987039 0 0 0 0 0 0 0 0 0 0 0 0 0 0.498229667608556 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.159132007464481 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.110433064086588 0 0 0 0 0 0 0 0 0 0.611004915345871 0 0 0 0.561899811991367 0 0 0;0 0 0 0.973527639623757 0 0 0 0 0 0 0 0 0.949329689605504 0 0 0 0 0 0 0;0 0 0 0 0.027077532916115 0 0 0 0 0 0.611004915345871 0.949329689605504 0 0 0 0 0 0.45822991145669 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.916926430883478 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0944227233455792;0 0 0 0 0 0 1.27266472620458 0 0 0 0.561899811991367 0 0 0 0 0 0 0.0123263072768274 0 0;0 0.786791961925432 0 0 0 0 0.665594866955372 0 0 0 0 0 0.45822991145669 0 0 0 0.0123263072768274 0 0.708053638455894 0;0 0 0 0 0.290796760442986 0 0 0.498229667608556 0 0 0 0 0 0.916926430883478 0 0 0 0.708053638455894 0 0;0 0 0.139982926657546 0 0 0 0.626568354787985 0 0 0 0 0 0 0 0 0.0944227233455792 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 4, [0 0 0 0 0 0 0 0 0 0.482056160228392 0 0 0 0 0 0 0 0 0.00508309589200806 0;0 0 0.342764171753101 0 0.592230924022738 0 0 0 0 0 0 0 0 0 0 0 0.616196530219501 0 0.105964156030294 0;0 0.342764171753101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.06756913797405 0 0 0 0 0;0 0.592230924022738 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 1.75169005582658 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.198256254136309 0 0.117040404671406 0.742253008190119 0 0 0 0 0 0 0 0 0;0 0 0 0 0 1.75169005582658 0.198256254136309 0 0 0.193487168668438 0 0 0 0 0 0.213470445629309 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.672768253513765 0 0 0 0 0 0 0 0;0.482056160228392 0 0 0 0 0 0.117040404671406 0.193487168668438 0 0 0.182397544709499 0 0 0 0 0 0 0 0.352313653363684 0;0 0 0 0 0 0 0.742253008190119 0 0 0.182397544709499 0 0.863897537114491 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0.672768253513765 0 0.863897537114491 0 0 0 0 0 0.849422540372472 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.126177070732391 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 1.06756913797405 0 0 0 0 0 0 0 0 0.126177070732391 0 0 0 0 0.810675752838635 0.192588746332897 0;0 0 0 0 0 0 0 0.213470445629309 0 0 0 0 0 0 0 0 0 0 0 0;0 0.616196530219501 0 0 0 0 0 0 0 0 0 0.849422540372472 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.810675752838635 0 0 0 0 0;0.00508309589200806 0.105964156030294 0 0 0 0 0 0 0 0.352313653363684 0 0 0 0 0.192588746332897 0 0 0 0 0.473102743220109;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.473102743220109 0] );\r\nassert( isequal(route,[5 2 19 15 4]) \u0026\u0026 abs( d - 1.95835 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 18, 3, [0 0 0 0 0 0 0.588131422298983 0 0 0 0 0 0 0 0 0 0 0 0.411615488806083 0;0 0 0 0 0 0 0 0 0 0.148093137302263 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.513126690185934 0.314081294727961 0 0 0 0 0 0 0 0 0 0 0.105622237791164 0 0;0 0 0 0 0.0233388222703319 0 0 0.620281238898666 0 0 0 1.01777898612281 0 0 1.02802169259185 0 0 0 0 0.829878923718159;0 0 0 0.0233388222703319 0 0.103664033766553 0 0 0 0 0.800687507355036 0.724909646173659 0 0 0 0 0 0 0 0;0 0 0.513126690185934 0 0.103664033766553 0 0 0 0 0 0 0 0.51932792913499 0.111508583765534 0 0 0 0 0 0;0.588131422298983 0 0.314081294727961 0 0 0 0 0 0.632615202648636 0 0 0 1.12039468709595 0 0 0 0 0 0 0;0 0 0 0.620281238898666 0 0 0 0 0.665853755155897 0 0.443519419164187 0 0.0287540443670959 0 0 0 0 0 0 0;0 0 0 0 0 0 0.632615202648636 0.665853755155897 0 0 0 0 0 0 0 0 0 0 0 0.341087071649035;0 0.148093137302263 0 0 0 0 0 0 0 0 0.0523829918450132 0 0 0 0 0 0 0.401940201122634 0.691832558529399 0;0 0 0 0 0.800687507355036 0 0 0.443519419164187 0 0.0523829918450132 0 0.491690939364275 0 0.757845451055127 0 0 0 0.024463668194654 0 0;0 0 0 1.01777898612281 0.724909646173659 0 0 0 0 0 0.491690939364275 0 0 0 0 0 0 0 0 1.02836268723464;0 0 0 0 0 0.51932792913499 1.12039468709595 0.0287540443670959 0 0 0 0 0 0 0 0 0 0 0 0.366143225287491;0 0 0 0 0 0.111508583765534 0 0 0 0 0.757845451055127 0 0 0 0.223088374978438 0 0 0 0 0;0 0 0 1.02802169259185 0 0 0 0 0 0 0 0 0 0.223088374978438 0 0.344909749483892 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.344909749483892 0 0 0 0.353158597614942 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.105622237791164 0 0 0 0 0 0 0.401940201122634 0.024463668194654 0 0 0 0 0 0 0 0 0;0.411615488806083 0 0 0 0 0 0 0 0 0.691832558529399 0 0 0 0 0 0.353158597614942 0 0 0 0.849677928981906;0 0 0 0.829878923718159 0 0 0 0 0.341087071649035 0 0 1.02836268723464 0.366143225287491 0 0 0 0 0 0.849677928981906 0] );\r\nassert( isequal(route,[18 3]) \u0026\u0026 abs( d - 0.105622 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 17, 23, [0 0.151141399637051 0 0 0 0 0 0 0 0 0 0.105216194021975 0 0 0 0 0 0.437381445735918 0 0 0.949941070771521 0 0 0 0;0.151141399637051 0 0 0 0 0 0 0 1.22583368379244 0 0 0.079829221583307 0.71041636270324 0 0 0 0.1075924794072 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 1.21948687450578 0 0.567263036089771 0 0 0 0 0 0.857795458880245 0 0 0 0 0.731569267553795 0 0 0;0 0 0 0 0 0 0.186039341309778 0 0 0 0 0 0 0 0 0 0 2.00348310018009 0 0 0 0 0 0 0;0 0 0 0 0 1.32070145417138 0 0 0 0 0 0 0 0 0 0 0 0.430765092398605 0 0 0 0 0 0.46856479561555 0;0 0 0 0 1.32070145417138 0 0 0.203767017753022 0 0 0 0 0 0 0.487213897131877 0 0 0.896335225888555 0 0 0 0 0 0 0;0 0 0 0.186039341309778 0 0 0 0 0 0 0 0 0.406945507381253 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.203767017753022 0 0 0.15769661193131 0 0.65001597680104 0 0 0 0 0 0 0.15666137867965 0 0 0 0 0.723509833846064 0 0;0 1.22583368379244 1.21948687450578 0 0 0 0 0.15769661193131 0 0 0 0 0 0 0 0 0.508542446617735 0 0 0.696934855529271 0.169312519482881 0 0.00704092733099992 0 0;0 0 0 0 0 0 0 0 0 0 0 0 1.08493079525094 0.214254564261975 0 0.425013648805044 0 0 0 0 0 0 0 0 0.00543872970590864;0 0 0.567263036089771 0 0 0 0 0.65001597680104 0 0 0 0 0 0 0 0 0.696979111426999 0.525282567629852 0 0.621146400617813 1.20050590589561 0 0 0 0;0.105216194021975 0.079829221583307 0 0 0 0 0 0 0 0 0 0 0.459862031186997 0 0 0 0 0 0 0.698687249438191 0 0 0.00200957746053532 0 0;0 0.71041636270324 0 0 0 0 0.406945507381253 0 0 1.08493079525094 0 0.459862031186997 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.214254564261975 0 0 0 0 0.380308744719566 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.487213897131877 0 0 0 0 0 0 0 0.380308744719566 0 0 0.0449909078512449 0 0 1.22341039971646 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.425013648805044 0 0 0 0 0 0 0 0 0 0 0 0 1.43760755773283 0.719177032769178 0;0 0.1075924794072 0.857795458880245 0 0 0 0 0 0.508542446617735 0 0.696979111426999 0 0 0 0.0449909078512449 0 0 0 0 0 0 0 0 0 0;0.437381445735918 0 0 2.00348310018009 0.430765092398605 0.896335225888555 0 0.15666137867965 0 0 0.525282567629852 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0.696934855529271 0 0.621146400617813 0.698687249438191 0 0 1.22341039971646 0 0 0 0 0 1.1519343197436 0 0 0 0;0.949941070771521 0 0 0 0 0 0 0 0.169312519482881 0 1.20050590589561 0 0 0 0 0 0 0 0 1.1519343197436 0 0 0 0 0.214330337662627;0 0 0.731569267553795 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.723509833846064 0.00704092733099992 0 0 0.00200957746053532 0 0 0 1.43760755773283 0 0 0 0 0 0 0 0 0;0 0 0 0 0.46856479561555 0 0 0 0 0 0 0 0 0 0 0.719177032769178 0 0 0 0 0 0 0 0 0.649429697531317;0 0 0 0 0 0 0 0 0 0.00543872970590864 0 0 0 0 0 0 0 0 0 0 0.214330337662627 0 0 0.649429697531317 0] );\r\nassert( isequal(route,[17 2 12 23]) \u0026\u0026 abs( d - 0.189431 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 2, 9, [0 0 0 0 0 0.390568942736463 0.253317405921882 0 0 0 0 0 0 0 0 0.035303866587423 0 0.0932427029924401 0 0.228317786309953 0 0 0 0 0;0 0 0 0 0 0 0.22325539367323 0.0737968434096563 0 0.0216391156829114 1.01817561468837 0 0 0.166613690540579 0 0 0 0 0 0.0929136548216463 0 0 0 0 0;0 0 0 0 0 0.324975382720294 0 0 0 0 0 0 0 0 0.257683850031889 0 0 0 0.818836795828793 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.0227807501033899 0 0 0 0 0.254392094407223 0 0 0 0 0 0.499630884751228 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.645309316664204 0 0 0 0 0 0.377002225744615 0 0 0 0 0;0.390568942736463 0 0.324975382720294 0 0 0 0 0 0 0 0 0.381625987614713 0.187530187877611 0 0 0 0 0 1.03662835165178 0 0 0 0 0 0;0.253317405921882 0.22325539367323 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.420116352005782 0.288516971344659 0 0 0 0 0.290423766876168 0;0 0.0737968434096563 0 0 0 0 0 0 0 0 0 0 0 1.14302504189143 0 0.894497023541543 0 0 0 0 0 0 0.181212342324972 0 0.4790219658659;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.66947645498941 0 0 0 0 0 0 0 0.881432911415172 0.190738003819626 0;0 0.0216391156829114 0 0 0 0 0 0 0 0 0 0 0 0 0 0.406383564162139 0 0 0 0 0 0 0 0 0.0727730905533963;0 1.01817561468837 0 0 0 0 0 0 0 0 0 0.968244418446258 0 0 0.528273242216383 0.125653272829919 0 0 0 0 0 0 0 0 0;0 0 0 0.0227807501033899 0 0.381625987614713 0 0 0 0 0.968244418446258 0 0 0 0 0.545221500471632 0 0 0.602095719309548 0 0 0 0 0 0;0 0 0 0 0 0.187530187877611 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.926256705235548 0 0 0 0;0 0.166613690540579 0 0 0.645309316664204 0 0 1.14302504189143 0 0 0 0 0 0 0 0 0 0.21560951711239 0 0 0 0 0 0 0;0 0 0.257683850031889 0 0 0 0 0 0.66947645498941 0 0.528273242216383 0 0 0 0 0 0 0 0 0 0.213055228450905 0 0 0 0;0.035303866587423 0 0 0 0 0 0 0.894497023541543 0 0.406383564162139 0.125653272829919 0.545221500471632 0 0 0 0 0.450996873658263 0 0 0 0 0 0 0 0;0 0 0 0.254392094407223 0 0 0 0 0 0 0 0 0 0 0 0.450996873658263 0 0 0 0 0 0 0.219529444302005 0 0;0.0932427029924401 0 0 0 0 0 0.420116352005782 0 0 0 0 0 0 0.21560951711239 0 0 0 0 0 0 0 0.863470148104069 0 0.444628451921207 0;0 0 0.818836795828793 0 0 1.03662835165178 0.288516971344659 0 0 0 0 0.602095719309548 0 0 0 0 0 0 0 0 0 0.109259232718513 0 0 0;0.228317786309953 0.0929136548216463 0 0 0.377002225744615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.615496286883062;0 0 0 0 0 0 0 0 0 0 0 0 0.926256705235548 0 0.213055228450905 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.863470148104069 0.109259232718513 0 0 0 0 0.0137884729469751 0;0 0 0 0.499630884751228 0 0 0 0.181212342324972 0.881432911415172 0 0 0 0 0 0 0 0.219529444302005 0 0 0 0 0 0 0.365687691203556 0;0 0 0 0 0 0 0.290423766876168 0 0.190738003819626 0 0 0 0 0 0 0 0 0.444628451921207 0 0 0 0.0137884729469751 0.365687691203556 0 0;0 0 0 0 0 0 0 0.4790219658659 0 0.0727730905533963 0 0 0 0 0 0 0 0 0 0.615496286883062 0 0 0 0 0] );\r\nassert( isequal(route,[2 7 24 9]) \u0026\u0026 abs( d - 0.704417 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 12, 4, [0 0 0 0 0 0.714172260222902 0 0 0 0 0 0 0 0 0 0 0 0 0 0.361655390928043 0 0 0 0 0;0 0 0 0 0 0.181492418867339 0 0 0 0 0 0 0 0 0 0 0 0 0.22307965545124 0 0 0 0 0 0.779096496125678;0 0 0 0 0 0 0.47292346615082 0 0 0.168841046075892 0 0 0 0 0 0 0 0 0 0.0217708463553388 0 0 0.667747131809592 0 0;0 0 0 0 0 0 0 0.57532352865356 0 0 1.14531063150095 0 0 0 0 0 0 0 0 1.81120439254402 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0.347520308679668 0 0 0 0 1.19301977580358 0 0.820270346367214 0 0 0.0596854204267023 0 0.365147722552969 0.160828167983497 0;0.714172260222902 0.181492418867339 0 0 0 0 0.993473525288174 0 0 0 0 0 0 0 0 0 0 0 0 0.900267645686867 0 0 0 0 0;0 0 0.47292346615082 0 0 0.993473525288174 0 0 0 0 0.303524976604928 0 0 0.988108387042638 0 0 0 0 1.09908596860658 0 0 0 0 0 0;0 0 0 0.57532352865356 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0496932258637628 0 0 0 0 0 0 0.220051533000778 0 0 0;0 0 0.168841046075892 0 0 0 0 0 0 0 0 0 0.415893111213311 0 0 0 0 0 0 0 0 0 0 0.252874847836154 0.7525160069564;0 0 0 1.14531063150095 0.347520308679668 0 0.303524976604928 0 0 0 0 0 0.315427799185682 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.0659140954680451 0.229430180128888 0 0 0 1.02421541676855 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.415893111213311 0.315427799185682 0 0 0 1.43148800286315 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.988108387042638 0 0 0 0 0.0659140954680451 0 0 0 0 0.0723048054691602 0.570598773372364 0 0 0 0 0 0 0.816798020448907;0 0 0 0 0 0 0 0 0.0496932258637628 0 0 0.229430180128888 1.43148800286315 0 0 0.0969052461008522 0 0 0.562394406606289 0 0 0 0 0 0;0 0 0 0 1.19301977580358 0 0 0 0 0 0 0 0 0 0.0969052461008522 0 0.830027198843182 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.0723048054691602 0 0.830027198843182 0 0 0 0 0.471570888980097 0 0 0 0;0 0 0 0 0.820270346367214 0 0 0 0 0 0 0 0 0.570598773372364 0 0 0 0 0 0 0 0 0 0 0;0 0.22307965545124 0 0 0 0 1.09908596860658 0 0 0 0 1.02421541676855 0 0 0.562394406606289 0 0 0 0 0 0 0 0 0 0;0.361655390928043 0 0.0217708463553388 1.81120439254402 0 0.900267645686867 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.0596854204267023 0 0 0 0 0 0 0 0 0 0 0 0.471570888980097 0 0 0 0 0.920738174557066 0 0 0;0 0 0 0 0 0 0 0 0.220051533000778 0 0 0 0 0 0 0 0 0 0 0 0.920738174557066 0 0 0 0;0 0 0.667747131809592 0 0.365147722552969 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.431353900603143;0 0 0 0 0.160828167983497 0 0 0 0 0.252874847836154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.139899289183316;0 0.779096496125678 0 0 0 0 0 0 0 0.7525160069564 0 0 0 0.816798020448907 0 0 0 0 0 0 0 0 0.431353900603143 0.139899289183316 0] );\r\nassert( isequal(route,[12 14 17 21 5 11 4]) \u0026\u0026 abs( d - 2.16231 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 21, 9, [0 1.30397831279197 0 0 0 0 0 0 0.205205702932437 0 0 0 0 0.462991342326146 0 0.0919395070383298 0 0 0 0 0 0 0 0.788285106392582 0;1.30397831279197 0 0 0 0 1.05960547137835 0 0 0.153644616706166 0 0 0.860262579703983 0 0 0 0 0 0 0 0 0 0 0 0.773468224481749 0;0 0 0 0.13195985000036 0 0 0 1.51813223209895 0 0 0 0 0 0.0156327604904785 0 1.27556519583119 0.93793259222666 0 0 0 0 0 0 0 0;0 0 0.13195985000036 0 0 0 0.458734989962526 0 0 0 0 0 0 0 0 0 0 0 0.835183344604242 0.11324698880843 0 0 1.27194671889278 0 0.873215204449672;0 0 0 0 0 0 0.0522387394084536 0.441514133149768 0 0 0 0 0 0 0 0 0 0 0 0.104845115642955 0 0 0 0 0;0 1.05960547137835 0 0 0 0 0 0 0 0 0 0.323427832607934 0 0 0 0 0 0.548178891330981 0 0 0 1.78927187214965 0 0 0;0 0 0 0.458734989962526 0.0522387394084536 0 0 0.128458273651367 0 1.10447307401052 0 0 1.60635812839544 0.490059715639469 0 0 0 0 0.87297695015148 0 0 0 0.0385154612576837 0 0;0 0 1.51813223209895 0 0.441514133149768 0 0.128458273651367 0 0 0.0426997868037813 0 0 0 0.316089962309322 0.556234063736422 0 0 0 0 0 0 0 0.125426946797567 0 0.501620852747415;0.205205702932437 0.153644616706166 0 0 0 0 0 0 0 0 0 0 0 0 0 1.22841179064666 0 0.506177174936367 0 0 0 0.139674374757514 0 0 0;0 0 0 0 0 0 1.10447307401052 0.0426997868037813 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.695460494256037;0 0 0 0 0 0 0 0 0 0 0 1.09170738934842 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.860262579703983 0 0 0 0.323427832607934 0 0 0 0 1.09170738934842 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 1.60635812839544 0 0 0 0 0 0 0 0 0 0 0 0 1.27248881503698 0 0 0 0 0.162219187624835;0.462991342326146 0 0.0156327604904785 0 0 0 0.490059715639469 0.316089962309322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.111140488918591 0.0639936929497993;0 0 0 0 0 0 0 0.556234063736422 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.0919395070383298 0 1.27556519583119 0 0 0 0 0 1.22841179064666 0 0 0 0 0 0 0 0.772788879713252 0 0 0 0.00743946114818939 0 0.662296573850469 0 0;0 0 0.93793259222666 0 0 0 0 0 0 0 0 0 0 0 0 0.772788879713252 0 0 0 0 0 0 0 0 0.235465388137087;0 0 0 0 0 0.548178891330981 0 0 0.506177174936367 0 0 0 0 0 0 0 0 0 0.596123003045261 0 0 0.50807778971881 0 0.192149930306311 0;0 0 0 0.835183344604242 0 0 0.87297695015148 0 0 0 0 0 0 0 0 0 0 0.596123003045261 0 1.75354812715354 0 0 0 0.230875543406997 0.402865723829543;0 0 0 0.11324698880843 0.104845115642955 0 0 0 0 0 0 0 1.27248881503698 0 0 0 0 0 1.75354812715354 0 0 0 0.286957051522517 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00743946114818939 0 0 0 0 0 0 0 0 0.12234328650336;0 0 0 0 0 1.78927187214965 0 0 0.139674374757514 0 0 0 0 0 0 0 0 0.50807778971881 0 0 0 0 0 0 0.0229366876888033;0 0 0 1.27194671889278 0 0 0.0385154612576837 0.125426946797567 0 0 0 0 0 0 0 0.662296573850469 0 0 0 0.286957051522517 0 0 0 0 0;0.788285106392582 0.773468224481749 0 0 0 0 0 0 0 0 0 0 0 0.111140488918591 0 0 0 0.192149930306311 0.230875543406997 0 0 0 0 0 0;0 0 0 0.873215204449672 0 0 0 0.501620852747415 0 0.695460494256037 0 0 0.162219187624835 0.0639936929497993 0 0 0.235465388137087 0 0.402865723829543 0 0.12234328650336 0.0229366876888033 0 0 0] );\r\nassert( isequal(route,[21 25 22 9]) \u0026\u0026 abs( d - 0.284954 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 5, [0 0 0.235687387990488 0 0 0.518662908555243 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00169033754843562 1.18573193396702 0 0 0 0 0;0.235687387990488 0 0 0 0.350144748614205 0 0 0.499051956190166 0 0 0 0 0 0 0 0.428154355783706 0 0 0 0.988646147648288 0 0 0 0 0.766292681932783;0 0 0 0 0 0 0 0 0 0.306976149669423 0 0 0 0 0 0 0.148608071246878 0 0 0 0 0 0 0 0;0 0 0.350144748614205 0 0 0 0.366820040758671 0 0 0 0.947779130029861 0 0 0 0 0 0 0 0 0.781367996905263 0 0 0 0 0;0.518662908555243 0 0 0 0 0 0.28467286265608 0 0 0 0 0 0 0 0 0 0 0 0.814169013559602 0 0 0 0 0 0.514510683872373;0 0 0 0 0.366820040758671 0.28467286265608 0 0 0 0 0 0.137928171247269 0 0 0 0 0 0.581896713318172 0 0 0 1.00288388568789 0.926366539848811 0 0;0 0 0.499051956190166 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.211128675902371 0 0 0 0 0 0 0 0 0;0 0 0 0.306976149669423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.332300868928405;0 0 0 0 0.947779130029861 0 0 0 0 0 0 0 0 0 0.140274584917356 0 0 0 0.276337784565307 0 0 0 0 0 0;0 0 0 0 0 0 0.137928171247269 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.422423933152413 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.16443124331918 0 0 0 0.113521569230241 0 0 0.431552467605628 0;0 0 0 0 0 0 0 0 0 0 0.140274584917356 0 0 0 0 0 0 1.01590071824433 0 0 0 0 0 0 0;0 0 0.428154355783706 0 0 0 0 0 0.211128675902371 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0.148608071246878 0 0 0 0 0 0 0 0 0 0.16443124331918 0 0 0 0 0 0.816401527141028 0 0 0 0 1.51727966787778;0 0 0 0 0 0 0.581896713318172 0 0 0 0 0 0 0 1.01590071824433 0 0 0 0 0 0.0315916186043663 0 0 0 0;0 0.00169033754843562 0 0 0 0.814169013559602 0 0 0 0 0.276337784565307 0 0.422423933152413 0 0 0 0 0 0 0 0 0 0 0.113122720118215 0;0 1.18573193396702 0.988646147648288 0 0.781367996905263 0 0 0 0 0 0 0 0 0 0 0 0.816401527141028 0 0 0 0 0 0 0 1.22595526329414;0 0 0 0 0 0 0 0 0 0 0 0 0 0.113521569230241 0 0 0 0.0315916186043663 0 0 0 0 0 0.0278718835255773 0;0 0 0 0 0 0 1.00288388568789 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.627233109842477 0 0;0 0 0 0 0 0 0.926366539848811 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.627233109842477 0 0.210579136296008 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.431552467605628 0 0 0 0 0.113122720118215 0 0.0278718835255773 0 0.210579136296008 0 0;0 0 0.766292681932783 0 0 0.514510683872373 0 0 0 0.332300868928405 0 0 0 0 0 0 1.51727966787778 0 0 1.22595526329414 0 0 0 0 0] );\r\nassert( isequal(route,5) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":26,"test_suite_updated_at":"2012-03-07T20:02:13.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-06T18:48:13.000Z","updated_at":"2026-03-30T17:22:55.000Z","published_at":"2012-03-07T20:03:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix that represent the distance along highways between major cities numbered 1 to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, provide the path and shortest distance from a given city,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrom\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, to a given city,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1169,"title":"Count the Number of Directed Cycles in a Graph","description":"Given an asymmetric adjacency matrix, determine the number of unique directed cycles.\r\nFor example, the graph represented by adjacency matrix\r\nA = [\r\n 0 1 1 0;\r\n 1 1 0 1;\r\n 1 0 0 1;\r\n 1 1 0 0];\r\nhas 7 cycles. They are:\r\n[2 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 1]\r\n[2 -\u003e 4 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 4 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 4 -\u003e 2 -\u003e 1]\r\nThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 399.2px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 199.6px; transform-origin: 407px 199.6px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 225.5px 8px; transform-origin: 225.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an asymmetric adjacency matrix, determine the number of unique\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.5px 8px; transform-origin: 29.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edirected\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e cycles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the graph represented by adjacency matrix\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 0 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehas 7 cycles. They are:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 143.033px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 71.5167px; transform-origin: 404px 71.5167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 32px 8.5px; tab-size: 4; transform-origin: 32px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 4 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; 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min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 2 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 380.5px 8px; transform-origin: 380.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nCycles = count_directed_cycles(A)\r\n nCycles = 0;\r\nend","test_suite":"%%\r\nA = [\r\n 0 1 1 0\r\n 1 1 0 1\r\n 1 0 0 1\r\n 1 1 0 0];\r\nnCycles = 7;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 0 1 0 1 0 0\r\n 0 0 0 1 1 0 1 1\r\n 1 1 0 1 1 0 1 1\r\n 0 1 0 0 1 0 1 0\r\n 1 1 0 0 1 0 1 0\r\n 1 0 0 0 0 0 1 0\r\n 1 1 1 1 0 0 0 1\r\n 0 0 0 1 1 1 1 1];\r\nnCycles = 197;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 1 0 1 0 0 1 1 0 1 1\r\n 1 0 1 0 1 1 1 1 1 1\r\n 0 0 1 0 0 1 1 1 1 1\r\n 0 1 0 0 0 0 0 0 1 1\r\n 0 0 1 0 1 0 1 1 1 0\r\n 1 1 1 0 0 0 0 1 0 0\r\n 0 0 0 1 0 1 1 0 1 0\r\n 1 0 1 1 0 0 0 0 1 0\r\n 1 0 0 0 0 0 1 0 0 1\r\n 1 0 0 0 0 0 0 0 1 0];\r\nnCycles = 744;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 1 0 1 1 1 1\r\n 0 0 0 1 0 1 0 1 1 1\r\n 0 0 0 0 0 0 1 1 1 1\r\n 0 0 1 0 1 1 0 1 0 0\r\n 0 1 0 1 0 0 0 0 0 1\r\n 1 0 0 1 1 1 1 0 1 1\r\n 0 1 1 1 1 0 1 1 0 1\r\n 0 1 0 0 1 1 0 0 0 1\r\n 1 0 1 0 0 1 1 0 1 1\r\n 1 0 1 1 0 1 1 0 0 0];\r\nnCycles = 4961;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 1 0 0 0 1 0\r\n 1 0 0 0 1 1 1 1\r\n 0 0 0 0 0 1 0 1\r\n 1 1 0 0 1 1 1 0\r\n 1 1 0 0 0 0 0 1\r\n 0 0 1 0 1 0 0 0\r\n 0 1 0 0 0 1 0 1\r\n 0 0 1 1 0 1 0 0];\r\nnCycles = 116;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 0 1 1 1 0 0 1 0\r\n 1 0 0 1 1 0 1 0 0 1\r\n 0 1 1 0 1 0 0 0 1 1\r\n 0 1 1 0 1 1 0 0 1 0\r\n 1 0 0 0 0 1 1 0 0 1\r\n 0 1 1 0 0 1 0 0 0 1\r\n 0 1 1 0 1 0 1 1 1 1\r\n 1 1 1 0 0 0 0 0 1 1\r\n 1 1 1 0 1 1 1 1 0 1\r\n 0 0 1 1 1 0 1 0 1 0];\r\nnCycles = 5888;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 1 1 0 1 0 1 1 0 0\r\n 0 0 1 1 1 0 0 0 0 0 0 1\r\n 1 0 1 0 0 0 1 1 1 0 1 1\r\n 1 0 1 1 0 0 0 0 1 0 0 0\r\n 1 1 1 0 1 0 0 0 1 0 1 0\r\n 0 1 0 1 0 0 0 0 1 1 0 0\r\n 1 1 1 1 1 0 1 0 1 0 1 0\r\n 0 1 0 1 0 0 1 1 1 1 1 1\r\n 0 1 0 1 1 0 0 1 0 1 1 0\r\n 0 1 1 1 1 1 0 0 0 1 1 0\r\n 1 1 1 1 0 0 1 1 0 0 0 0\r\n 0 1 1 1 1 1 1 1 0 0 0 1];\r\nnCycles = 50093;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":1057,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":"2021-12-31T15:51:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T13:38:03.000Z","updated_at":"2026-04-04T08:59:38.000Z","published_at":"2013-01-04T13:48:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an asymmetric adjacency matrix, determine the number of unique\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edirected\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cycles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the graph represented by adjacency matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [\\n 0 1 1 0;\\n 1 1 0 1;\\n 1 0 0 1;\\n 1 1 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehas 7 cycles. They are:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[2 -\u003e 2]\\n[1 -\u003e 2 -\u003e 1]\\n[1 -\u003e 3 -\u003e 1]\\n[2 -\u003e 4 -\u003e 2]\\n[1 -\u003e 2 -\u003e 4 -\u003e 1]\\n[1 -\u003e 3 -\u003e 4 -\u003e 1]\\n[1 -\u003e 3 -\u003e 4 -\u003e 2 -\u003e 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56020,"title":"Easy Sequences 72: Complete Graph","description":"In Graph Theory, we say a graph is a Complete Graph, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\r\n \r\nIn this problem we are asked to calculate , which is the number of connections of a complete graph with nodes.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440001px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 406px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 203px; transform-origin: 407px 203px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn Graph Theory, we say a graph is a \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eComplete Graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 324px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 162px; text-align: left; transform-origin: 384px 162px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"359\" height=\"318\" style=\"vertical-align: 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 11px; text-align: left; transform-origin: 384px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem we are asked to calculate \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"18\" height=\"20\" style=\"width: 18px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, which is the number of connections of a complete graph with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e nodes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k = K(n)\r\n y = x;\r\nend","test_suite":"%%\r\nn = '12';\r\nk_correct = '66';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '1234';\r\nk_correct = '760761';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '123456789';\r\nk_correct = '7620789313366866';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '555555555555';\r\nk_correct = '154320987653734567901235';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '8888888888888888';\r\nk_correct = '39506172839506160493827160493828';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '33333333333333333333';\r\nk_correct = '555555555555555555527777777777777777778';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = repmat('987654321',1,5);\r\nk_correct = '487730529870446579753162629635878679518594727932022558549306508666590458783874408901158360';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = repmat('1',1,50);\r\nk_correct = '61728395061728395061728395061728395061728395061721604938271604938271604938271604938271604938271605';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn1 = repmat('2',1,20);\r\nn2 = repmat('3',1,20);\r\nn3 = repmat('4',1,20);\r\nk_correct = [160 161 162 164 155 156 157 159 149 160 161 162 164 155 156 157 159 149 160 153 156 165 164 164 152 161 160 159 158 156 165 164 164 152 161 160 159 158 159];\r\nassert(isequal(K(n1)+K(n2)+K(n3),k_correct))\r\n%%\r\nfiletext = fileread('K.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'BigInteger') || contains(filetext, 'java') ...\r\n|| contains(filetext, 'sum') || contains(filetext, 'prod') || contains(filetext, 'if') || contains(filetext, 'for') ... \r\n|| contains(filetext, 'switch') || contains(filetext, 'while') || contains(filetext, 'try') || contains(filetext, 'repmat') ...\r\n|| contains(filetext, '{') || contains(filetext, '}') || contains(filetext, 'cell');\r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":255988,"edited_by":255988,"edited_at":"2022-09-22T13:34:32.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-22T09:44:22.000Z","updated_at":"2022-09-22T13:34:32.000Z","published_at":"2022-09-22T11:09:35.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn Graph Theory, we say a graph is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eComplete Graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"318\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"359\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem we are asked to calculate \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eK_n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, which is the number of connections of a complete graph with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2732,"title":"Construct a precedence graph from a code segment","description":"A hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\r\n\r\nNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\r\n\r\nAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\r\n\r\nExample:\r\n\r\n C = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\n\r\nHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\r\n\r\nThus the resulting adjacency matrix will be as follows:\r\n\r\n \r\n mat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\n\r\n\r\n\r\n\r\nDefinition of adjacency matrix:\r\n\u003chttp://en.wikipedia.org/wiki/Adjacency_matrix\u003e","description_html":"\u003cp\u003eA hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\u003c/p\u003e\u003cp\u003eNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\u003c/p\u003e\u003cp\u003eAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eC = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\n\u003c/pre\u003e\u003cp\u003eHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\u003c/p\u003e\u003cp\u003eThus the resulting adjacency matrix will be as follows:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\n\u003c/pre\u003e\u003cp\u003eDefinition of adjacency matrix: \u003ca href = \"http://en.wikipedia.org/wiki/Adjacency_matrix\"\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/a\u003e\u003c/p\u003e","function_template":"function y = pGraph(x)\r\n\r\n\r\n\r\n\r\nend","test_suite":"%%\r\nC = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\nmat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n\r\n\r\n%%\r\nC = {'a=1;'\r\n 'a=1;'\r\n 'c=1;'\r\n 'c=1;'};\r\nmat = [0 0 0 0;\r\n 0 0 0 0;\r\n 0 0 0 0;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n%%\r\nC = {'a=1;'\r\n 'a=1;'\r\n 'c=a+1;'\r\n 'c=a+1;'};\r\nmat = [0 0 0 0;\r\n 0 0 1 1;\r\n 0 0 0 0;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n%%\r\nC = {'a=1;'\r\n 'b=a+2;'\r\n 'c=b+1;'\r\n 'd=c+1;'};\r\nmat = double(~tril(ones(4)))\r\nassert(isequal(pGraph(C),mat))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2014-12-06T07:56:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-12-06T07:54:40.000Z","updated_at":"2024-11-02T13:28:43.000Z","published_at":"2014-12-06T07:54:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = {'a=1;'\\n 'b=1;'\\n 'c=a+b;'\\n 'c=c+1;'};]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThus the resulting adjacency matrix will be as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mat = [0 0 1 1;\\n 0 0 1 1;\\n 0 0 0 1;\\n 0 0 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDefinition of adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Adjacency_matrix\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":283,"title":"Give the Shortest Path Through The Maze","description":"*Description*\r\n\r\nThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size _M_ x _N_ where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\r\n\r\n + +\r\n value = 0 -\u003e 00 -\u003e no walls -\u003e \r\n NW + + \r\n\r\n + +\r\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\r\n NW + +\r\n \r\n +---+\r\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \r\n NW + +\r\n\r\n +---+\r\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\r\n + +\r\n\r\n_Note: all outer boundaries are walls. My test cases provide for this already. You do *not* need to account for this._\r\n\r\nThe path will always start at the NorthWest corner (subscript |(1,1)|) and end at the SouthEast corner (subscript |(M,N)|). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\r\n\r\n path = [ 1 2 0 0 0 0 \r\n 4 3 0 9 10 0\r\n 5 6 7 8 11 0 \r\n 0 0 0 0 12 13 ]\r\n\r\nwhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\r\n\r\nYou are *NOT* guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\r\n\r\n*Example*\r\n\r\nInput maze:\r\n\r\n maze = [ 3 2 2 2 3\r\n 1 3 2 2 3\r\n 1 3 2 3 1\r\n 1 1 0 2 0\r\n 1 0 2 1 1 ];\r\n \r\nGraphical Representation:\r\n\r\n +---+---+---+---+---+\r\n | | |\r\n + +---+---+---+---+\r\n | | | |\r\n + +---+---+---+ +\r\n | | | | |\r\n + + + +---+ +\r\n | | |\r\n + + +---+ + +\r\n | | | |\r\n +---+---+---+---+---+\r\n\r\nSolution:\r\n\r\n soln = [ 1 0 0 0 0\r\n 2 0 0 0 0\r\n 3 0 0 0 0\r\n 4 7 8 9 10\r\n 5 6 0 0 11 ]\r\n\r\nGraphical Representation:\r\n\r\n \r\n +---+---+---+---+---+\r\n | 1 | |\r\n + +---+---+---+---+\r\n | 2 | | |\r\n + +---+---+---+ +\r\n | 3 | | | |\r\n + + + +---+ +\r\n | 4 | 7 8 9 10 |\r\n + + +---+ + +\r\n | 5 6 | |11 |\r\n +---+---+---+---+---+","description_html":"\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size \u003ci\u003eM\u003c/i\u003e x \u003ci\u003eN\u003c/i\u003e where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\u003c/p\u003e\u003cpre\u003e + +\r\n value = 0 -\u003e 00 -\u003e no walls -\u003e \r\n NW + + \u003c/pre\u003e\u003cpre\u003e + +\r\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\r\n NW + +\u003c/pre\u003e\u003cpre\u003e +---+\r\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \r\n NW + +\u003c/pre\u003e\u003cpre\u003e +---+\r\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\r\n + +\u003c/pre\u003e\u003cp\u003e\u003ci\u003eNote: all outer boundaries are walls. My test cases provide for this already. You do \u003cb\u003enot\u003c/b\u003e need to account for this.\u003c/i\u003e\u003c/p\u003e\u003cp\u003eThe path will always start at the NorthWest corner (subscript \u003ctt\u003e(1,1)\u003c/tt\u003e) and end at the SouthEast corner (subscript \u003ctt\u003e(M,N)\u003c/tt\u003e). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\u003c/p\u003e\u003cpre\u003e path = [ 1 2 0 0 0 0 \r\n 4 3 0 9 10 0\r\n 5 6 7 8 11 0 \r\n 0 0 0 0 12 13 ]\u003c/pre\u003e\u003cp\u003ewhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\u003c/p\u003e\u003cp\u003eYou are \u003cb\u003eNOT\u003c/b\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInput maze:\u003c/p\u003e\u003cpre\u003e maze = [ 3 2 2 2 3\r\n 1 3 2 2 3\r\n 1 3 2 3 1\r\n 1 1 0 2 0\r\n 1 0 2 1 1 ];\u003c/pre\u003e\u003cp\u003eGraphical Representation:\u003c/p\u003e\u003cpre\u003e +---+---+---+---+---+\r\n | | |\r\n + +---+---+---+---+\r\n | | | |\r\n + +---+---+---+ +\r\n | | | | |\r\n + + + +---+ +\r\n | | |\r\n + + +---+ + +\r\n | | | |\r\n +---+---+---+---+---+\u003c/pre\u003e\u003cp\u003eSolution:\u003c/p\u003e\u003cpre\u003e soln = [ 1 0 0 0 0\r\n 2 0 0 0 0\r\n 3 0 0 0 0\r\n 4 7 8 9 10\r\n 5 6 0 0 11 ]\u003c/pre\u003e\u003cp\u003eGraphical Representation:\u003c/p\u003e\u003cpre\u003e +---+---+---+---+---+\r\n | 1 | |\r\n + +---+---+---+---+\r\n | 2 | | |\r\n + +---+---+---+ +\r\n | 3 | | | |\r\n + + + +---+ +\r\n | 4 | 7 8 9 10 |\r\n + + +---+ + +\r\n | 5 6 | |11 |\r\n +---+---+---+---+---+\u003c/pre\u003e","function_template":"function path = solve_maze(maze)\r\n path = zeros( size( maze ) );\r\nend","test_suite":"%%\r\nassert(isequal(solve_maze([3 2;1 2]),[1 0;2 3]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2;3 3 1;1 1 1]),[1 2 3;0 0 4;0 0 5]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 3 2 2 2;1 0 1 2 1;3 2 3 3 0;1 0 2 2 0;1 1 1 0 1]),[1 4 5 6 7;2 3 0 0 8;0 0 0 0 9;0 0 0 0 10;0 0 0 0 11]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 3 3 2 3;3 1 2 1 1 3 1 3;1 0 3 0 0 1 3 3;1 1 0 2 0 0 1 1;3 1 3 3 2 1 2 1;3 2 2 1 3 0 1 0;3 3 2 3 1 2 2 0;1 3 3 0 0 2 0 2;1 2 3 1 0 2 1 3;1 1 3 2 3 1 1 0]),[1 2 0 0 0 0 0 0;0 3 0 0 0 0 0 0;0 4 0 0 0 0 0 0;0 5 6 7 8 9 0 0;0 0 0 0 0 10 11 0;0 0 0 0 12 11 12 13;0 0 0 0 13 14 15 14;0 0 0 0 14 15 16 0;0 0 0 0 0 0 17 0;0 0 0 0 0 0 18 19]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 2 3 3 2 3 3 3 2 3 2 3 2 3;3 0 3 3 1 2 2 3 3 0 2 1 0 1 2 2 0;1 0 2 3 2 1 1 0 0 3 1 0 2 1 1 0 2;3 1 1 2 0 0 2 0 2 3 3 1 0 3 1 0 3;1 0 1 0 3 0 1 0 0 2 2 1 0 2 1 2 1;1 2 0 0 3 1 2 1 2 3 1 1 0 1 3 1 1;3 1 1 3 2 2 2 2 2 1 2 3 2 0 3 1 2;1 3 0 1 0 3 0 2 1 3 2 2 2 1 1 1 2;1 3 3 1 1 1 2 3 2 2 2 2 3 3 2 0 1;3 1 3 0 2 0 2 0 1 0 3 1 2 3 2 0 0;1 1 0 2 3 3 1 2 2 1 3 0 2 3 2 2 0;3 0 0 2 3 3 0 0 2 2 0 0 1 3 1 3 1]),[1 2 0 0 0 0 0 0 0 0 0 0 21 22 0 0 0;0 3 0 0 0 0 0 0 14 15 16 19 20 23 24 0 0;0 4 5 0 0 0 0 12 13 0 17 18 0 0 25 0 0;0 0 6 7 8 9 10 11 0 0 0 0 0 0 26 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 28 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 29 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 32;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 33;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 34;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 3 3 2 2 2 3 2 3 3 2 2 2 2 2 2 2 2 3 3 2 3 2 3;1 2 2 2 1 1 1 3 0 3 1 1 0 2 3 0 2 0 0 3 3 0 3 1 3;3 1 3 2 3 1 2 2 2 3 3 0 3 3 3 3 3 3 0 0 2 0 3 0 3;3 1 1 1 3 3 3 1 0 1 1 3 3 2 2 2 3 1 3 1 3 1 3 1 0;3 1 3 0 1 3 2 1 1 2 1 0 3 1 3 2 1 0 1 0 2 0 3 2 0;3 0 2 2 2 3 2 1 2 2 0 3 0 0 1 1 0 1 3 1 3 0 2 0 0;3 3 0 0 2 0 3 0 1 1 2 3 1 0 3 1 3 3 0 1 2 3 1 3 2;1 0 2 0 3 1 1 0 3 2 0 0 0 0 0 0 1 3 3 2 2 0 0 2 2;1 2 1 3 2 1 3 1 0 3 2 3 3 3 1 0 1 2 0 1 3 3 0 2 1;1 0 3 2 0 0 3 3 2 2 0 0 3 3 0 1 2 0 2 1 2 0 2 3 3;3 0 2 3 3 2 3 2 1 3 3 1 2 0 3 2 1 0 2 0 1 3 1 2 0;1 0 1 1 2 2 1 2 2 1 3 1 0 3 3 2 3 2 1 2 3 3 3 1 2;1 2 1 0 0 3 3 3 0 1 2 1 1 2 2 0 2 3 3 3 1 0 3 1 3;3 3 2 3 1 2 2 2 1 0 2 0 3 1 3 0 0 2 1 3 0 1 3 0 1;1 2 2 0 0 3 1 1 1 3 1 0 3 3 1 0 2 0 3 3 1 0 0 0 3;1 2 1 2 1 1 0 0 2 0 3 0 0 1 0 0 1 3 0 0 1 3 2 0 1;3 3 1 2 0 1 1 1 1 2 2 0 1 2 1 1 3 0 0 0 1 1 3 0 0;3 2 2 0 0 0 0 3 2 2 1 1 0 0 2 3 1 3 3 3 0 3 1 3 0;1 2 2 0 3 1 2 0 3 1 3 2 3 3 2 1 2 3 3 2 2 1 2 0 0;1 3 1 3 2 1 3 1 2 1 0 0 0 2 1 3 0 3 0 2 3 0 0 0 0]),[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 7 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 10 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;12 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;13 14 0 0 0 0 0 0 0 0 0 0 0 0 43 44 0 0 0 0 0 0 0 0 0;14 15 0 0 0 0 0 0 0 0 0 0 0 41 42 45 46 47 0 51 52 53 54 0 0;0 16 17 0 0 0 0 0 0 0 0 38 39 40 0 0 47 48 49 50 0 0 55 56 0;0 0 18 0 0 0 0 0 0 0 0 37 38 0 0 0 0 0 0 0 0 0 0 57 0;0 0 19 20 21 0 0 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0 58 0;0 0 0 0 22 23 24 25 0 0 0 35 0 0 0 0 0 0 0 0 0 0 0 59 0;0 0 0 0 0 0 25 26 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 60 0;0 0 0 0 0 0 26 27 28 0 0 33 0 0 0 0 0 0 0 0 0 0 0 61 0;0 0 0 0 0 0 0 0 29 30 31 32 0 0 0 0 0 0 0 0 0 0 0 62 63;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 66]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 3 3 2 3 2 3 2 3 2 3 2 2 2 2 3 3 3 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 3 3 3 2 2 3 2 3;3 2 0 1 3 2 1 0 1 2 2 1 2 1 1 0 2 1 0 2 1 3 3 2 3 3 0 0 3 2 3 1 3 1 3 3 0 3 1 0 0 2 1 1 0;1 1 3 0 3 3 0 1 2 2 3 0 0 2 2 3 3 0 0 1 3 3 3 1 3 2 3 1 3 3 1 0 0 0 0 2 2 2 0 3 3 0 1 1 2;1 0 2 2 0 1 0 2 2 3 2 1 0 3 1 3 1 1 1 3 3 2 1 1 2 0 1 1 2 1 3 0 3 3 0 1 1 2 2 3 1 1 1 1 3;1 1 0 0 0 3 0 2 2 0 0 2 1 3 2 3 2 1 3 2 0 2 3 0 0 0 3 0 0 1 0 1 0 2 0 1 1 1 2 3 0 2 1 3 1;3 3 1 1 2 0 3 0 0 3 2 1 1 1 3 1 3 0 1 0 1 0 3 1 3 1 2 3 1 2 1 1 3 3 2 1 2 1 3 0 2 3 2 0 2;3 1 2 0 0 3 3 1 1 2 1 0 3 3 3 0 2 1 3 0 3 0 0 2 2 3 2 1 0 1 3 2 0 2 3 3 3 0 1 2 1 1 1 0 2;3 1 2 0 0 3 1 0 3 3 2 3 2 0 2 3 2 2 0 0 2 0 1 1 3 1 1 3 2 0 1 0 0 2 2 1 2 1 0 0 3 0 2 2 1;3 0 0 2 0 0 1 0 3 2 2 0 0 3 1 1 2 0 0 0 3 2 0 2 1 1 2 0 3 2 3 2 0 3 3 3 1 3 2 1 0 2 0 1 2;1 0 1 0 1 2 3 2 0 1 0 0 0 1 1 2 1 3 0 2 0 0 0 3 2 0 0 1 2 0 2 1 2 3 2 1 0 3 2 0 2 1 1 2 2;3 0 0 1 0 2 0 2 0 2 3 2 3 0 2 3 3 1 0 2 3 2 0 3 0 0 2 0 2 3 0 3 1 0 3 1 0 2 2 1 0 1 2 2 3;1 3 3 1 2 3 1 2 2 3 3 3 3 0 2 1 2 1 1 3 0 2 2 2 0 3 3 2 0 3 0 0 0 0 3 2 2 0 0 1 3 3 0 0 0;1 0 3 1 2 1 0 3 0 0 0 0 3 2 3 0 2 2 0 0 1 1 1 2 3 3 1 0 2 1 2 1 3 1 2 0 2 3 0 0 0 3 1 0 3;3 2 0 0 1 1 2 3 1 1 2 1 2 2 0 3 2 1 2 0 0 1 3 1 3 2 2 3 3 3 3 0 3 1 2 0 0 1 1 3 3 2 2 1 2;1 3 3 1 3 0 0 2 0 3 0 3 1 2 2 3 0 3 0 2 0 3 1 1 0 2 3 2 1 2 0 0 2 0 3 1 0 3 2 1 0 1 1 1 2;3 1 3 3 2 1 2 1 1 3 1 3 1 0 3 3 2 3 1 3 0 0 1 2 1 3 0 2 2 2 2 1 2 3 2 0 3 2 0 2 1 2 1 1 2;1 2 2 1 3 2 0 3 1 1 1 0 2 3 1 2 0 0 2 0 0 2 3 2 3 1 1 3 2 1 1 3 3 3 0 1 3 3 1 0 2 0 1 1 0;1 2 0 0 2 1 1 0 2 2 0 2 0 3 1 2 2 0 2 0 3 2 0 0 2 1 1 1 0 1 2 0 0 2 3 1 0 0 1 2 0 3 2 1 2;1 2 2 1 0 2 1 1 3 1 2 3 1 2 2 3 1 2 2 2 1 1 0 0 1 2 0 2 0 3 2 1 0 2 3 1 1 0 2 1 1 0 0 0 2;3 0 2 0 1 1 2 0 3 3 3 3 0 1 0 1 3 1 1 2 1 1 1 3 0 0 3 0 0 3 1 0 2 3 2 0 2 2 0 3 0 1 1 3 0;3 1 3 0 2 1 3 1 0 3 0 3 1 2 1 3 2 3 0 1 0 3 3 3 0 1 2 0 0 1 2 2 3 2 0 2 1 2 2 3 1 0 0 3 1;3 1 2 0 1 2 3 0 1 0 0 1 2 0 1 1 1 2 1 1 3 1 1 2 0 1 3 0 3 0 0 0 0 1 1 2 1 3 0 2 0 3 2 2 1;3 0 2 1 0 3 2 2 1 0 2 3 1 1 2 1 1 1 1 3 3 2 2 3 2 1 2 2 0 1 3 3 0 3 3 0 1 2 0 0 3 2 1 0 1;3 3 2 2 3 3 2 0 2 1 1 3 3 3 1 2 0 3 0 1 1 3 3 1 0 3 0 1 0 2 1 2 2 0 0 0 2 1 2 1 0 3 0 3 2;1 0 3 2 1 0 2 3 1 1 3 1 3 2 2 2 3 0 2 1 3 2 1 1 1 3 1 0 0 0 0 0 3 2 3 3 3 1 2 2 1 2 0 3 0;1 0 3 3 3 2 2 3 0 0 0 3 3 3 0 0 3 3 0 0 2 3 1 3 3 3 1 1 3 2 3 0 0 2 2 1 0 0 2 3 3 3 3 3 0;3 0 1 1 0 1 2 3 1 3 1 3 0 3 3 1 2 3 3 2 3 3 2 3 1 3 2 1 0 1 0 1 3 1 0 2 2 0 0 3 1 0 3 0 0;1 3 3 2 2 2 1 0 1 0 3 3 2 0 2 3 0 1 0 0 3 1 0 1 2 1 0 2 1 3 2 3 2 0 0 2 0 3 2 1 3 1 0 0 3;3 2 3 2 2 3 1 3 0 1 2 0 3 3 2 3 3 1 0 1 1 1 0 2 2 2 3 3 2 0 2 0 1 1 0 2 0 1 0 2 1 0 3 2 0;3 0 3 1 0 3 3 3 0 2 1 2 2 2 0 2 0 3 1 1 0 2 2 0 0 2 2 1 0 1 0 0 2 3 3 0 0 0 0 2 2 0 3 2 0;3 1 3 0 1 2 3 3 1 1 3 1 2 0 1 1 0 3 1 1 0 2 3 1 3 2 1 1 3 0 3 3 3 0 1 3 2 0 0 3 2 3 2 0 1;3 2 2 2 2 3 3 0 1 2 1 2 3 3 2 2 2 2 3 0 1 2 0 3 2 2 3 0 2 2 3 1 1 0 0 3 2 3 0 0 1 2 2 0 0;3 1 3 2 1 3 1 2 3 2 2 2 2 0 1 3 0 2 3 0 0 3 3 2 3 3 1 2 1 1 0 0 3 2 0 3 0 2 1 0 2 3 2 1 3;3 1 0 0 1 2 3 2 2 3 1 0 1 2 2 0 3 2 1 0 1 1 0 2 1 3 0 1 1 0 1 2 1 3 1 3 3 0 2 0 0 1 0 0 2;3 0 0 3 0 2 2 1 1 0 0 2 0 3 0 2 1 0 3 2 0 3 3 3 3 0 3 0 0 1 2 0 1 3 2 3 0 3 0 2 3 1 2 0 0;3 3 0 2 0 0 2 0 0 0 1 2 2 0 0 1 0 2 2 1 0 2 2 2 3 3 2 3 0 2 0 2 1 2 0 1 1 3 3 0 3 2 1 2 3;3 2 0 2 1 3 3 0 3 0 1 1 0 3 0 2 0 3 1 2 0 0 3 2 2 1 1 2 1 0 2 0 0 1 3 2 2 2 2 1 2 1 2 3 1;1 3 1 2 2 0 2 0 3 3 3 2 1 0 2 3 1 2 3 3 2 3 2 1 2 0 3 3 1 1 1 0 3 2 0 0 1 2 2 2 0 2 1 3 3;1 3 3 3 0 0 3 2 2 1 2 0 2 3 1 2 0 2 1 3 3 2 0 1 3 0 0 2 3 1 3 3 1 0 0 0 2 2 3 3 2 0 1 2 1;3 3 2 0 0 0 1 2 0 0 3 1 2 1 0 1 0 2 1 0 2 1 2 2 2 1 0 1 1 3 1 3 2 3 3 1 3 2 0 3 3 0 2 2 2;1 1 1 0 3 1 0 0 3 2 3 2 2 0 2 2 3 0 1 2 0 1 3 3 2 1 3 1 1 0 2 1 0 0 0 0 2 3 1 3 0 1 1 2 0;3 1 0 1 0 1 3 3 2 2 3 2 1 3 2 0 1 1 2 0 0 2 2 2 2 1 1 3 3 1 0 3 0 2 0 1 2 2 2 2 2 0 1 3 1;1 0 3 1 1 2 3 1 3 1 0 1 1 1 3 0 2 3 1 3 1 0 3 3 3 2 0 1 3 3 1 3 0 0 2 3 0 2 3 1 3 2 1 3 2;3 0 3 2 3 3 2 3 3 1 0 0 3 2 0 1 0 3 1 1 2 0 0 3 2 1 1 2 2 1 1 1 2 1 3 1 3 3 3 2 0 1 0 2 2;1 3 3 1 3 2 2 3 2 0 2 3 3 1 3 1 0 3 0 1 2 2 1 1 1 2 2 2 0 3 0 2 1 0 3 1 2 0 1 0 0 3 0 3 0;3 2 3 1 0 0 0 0 2 3 1 3 3 2 2 1 2 1 3 2 2 1 0 2 2 1 0 3 1 3 1 3 2 0 1 3 2 2 3 0 2 1 3 1 3;1 3 3 1 3 0 3 1 0 3 1 1 2 0 3 2 1 2 1 2 3 0 1 2 3 0 1 2 1 0 0 3 1 3 0 2 3 2 2 2 0 1 0 3 3;3 0 0 0 0 0 3 0 0 1 3 0 1 3 3 3 1 0 1 2 1 0 1 0 3 1 0 1 2 1 3 1 0 2 0 3 0 3 0 2 2 0 2 0 3;3 2 0 3 2 3 3 1 1 3 0 0 0 3 3 2 2 3 1 3 3 2 1 3 3 2 0 2 0 3 2 0 1 1 2 2 1 1 2 0 0 2 0 1 1;1 1 1 2 0 1 0 0 2 0 2 2 3 3 3 3 0 3 0 3 1 3 3 1 3 0 3 3 0 1 0 1 1 2 2 0 3 3 0 3 0 3 3 0 0]),[1 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 8 9 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 11 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 12 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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110]))","published":true,"deleted":false,"likes_count":14,"comments_count":0,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":57,"test_suite_updated_at":"2012-02-08T22:51:09.000Z","rescore_all_solutions":false,"group_id":33,"created_at":"2012-02-08T03:01:26.000Z","updated_at":"2026-02-07T15:43:43.000Z","published_at":"2012-02-09T00:21:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ + +\\n value = 0 -\u003e 00 -\u003e no walls -\u003e \\n NW + + \\n\\n + +\\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\\n NW + +\\n\\n +---+\\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \\n NW + +\\n\\n +---+\\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\\n + +]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote: all outer boundaries are walls. My test cases provide for this already. You do\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e need to account for this.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe path will always start at the NorthWest corner (subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(1,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) and end at the SouthEast corner (subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(M,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ path = [ 1 2 0 0 0 0 \\n 4 3 0 9 10 0\\n 5 6 7 8 11 0 \\n 0 0 0 0 12 13 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput maze:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ maze = [ 3 2 2 2 3\\n 1 3 2 2 3\\n 1 3 2 3 1\\n 1 1 0 2 0\\n 1 0 2 1 1 ];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGraphical Representation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ +---+---+---+---+---+\\n | | |\\n + +---+---+---+---+\\n | | | |\\n + +---+---+---+ +\\n | | | | |\\n + + + +---+ +\\n | | |\\n + + +---+ + +\\n | | | |\\n +---+---+---+---+---+]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolution:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ soln = [ 1 0 0 0 0\\n 2 0 0 0 0\\n 3 0 0 0 0\\n 4 7 8 9 10\\n 5 6 0 0 11 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGraphical Representation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ +---+---+---+---+---+\\n | 1 | |\\n + +---+---+---+---+\\n | 2 | | |\\n + +---+---+---+ +\\n | 3 | | | |\\n + + + +---+ +\\n | 4 | 7 8 9 10 |\\n + + +---+ + +\\n | 5 6 | |11 |\\n +---+---+---+---+---+]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44378,"title":"Five-dimensional maze","description":"*Description*\r\n\r\nThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes _x_ and _y_. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\r\n\r\nThis problem is a generalization of \u003chttps://www.mathworks.com/matlabcentral/cody/problems/283 Problem 283\u003e. If you haven't solved that yet, I would recommend solving it first.\r\n\r\n*Encoding*\r\n\r\n* The maze will be represented by an [ _M_ x _N_ x _O_ x _P_ x _Q_ ] matrix. \r\n* Each element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\r\n* Walls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\r\n* The start position is at the origin: subscript |(1,1,1,1,1)|.\r\n* The end position is at the furthest extent: subscript |(M,N,O,P,Q)|.\r\n* The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to \u003chttps://www.mathworks.com/matlabcentral/cody/problems/283 Problem 283\u003e for a 2-D example.\r\n* You are *NOT* guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.","description_html":"\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes \u003ci\u003ex\u003c/i\u003e and \u003ci\u003ey\u003c/i\u003e. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\u003c/p\u003e\u003cp\u003eThis problem is a generalization of \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/283\"\u003eProblem 283\u003c/a\u003e. If you haven't solved that yet, I would recommend solving it first.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEncoding\u003c/b\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003eThe maze will be represented by an [ \u003ci\u003eM\u003c/i\u003e x \u003ci\u003eN\u003c/i\u003e x \u003ci\u003eO\u003c/i\u003e x \u003ci\u003eP\u003c/i\u003e x \u003ci\u003eQ\u003c/i\u003e ] matrix.\u003c/li\u003e\u003cli\u003eEach element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\u003c/li\u003e\u003cli\u003eWalls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\u003c/li\u003e\u003cli\u003eThe start position is at the origin: subscript \u003ctt\u003e(1,1,1,1,1)\u003c/tt\u003e.\u003c/li\u003e\u003cli\u003eThe end position is at the furthest extent: subscript \u003ctt\u003e(M,N,O,P,Q)\u003c/tt\u003e.\u003c/li\u003e\u003cli\u003eThe output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/283\"\u003eProblem 283\u003c/a\u003e for a 2-D example.\u003c/li\u003e\u003cli\u003eYou are \u003cb\u003eNOT\u003c/b\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/li\u003e\u003c/ul\u003e","function_template":"function path = solve_maze5(maze)\r\n path = zeros(size(maze));\r\n path(1) = 1;\r\nend","test_suite":"%%\r\nmaze = reshape([15 15 15 15 15 15 15 15 15 31], [1 1 1 1 10]);\r\ntruth = reshape([1 2 3 4 5 6 7 8 9 10], [1 1 1 1 10]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([28 28 30 28 30 30 29 30 28 29 29 30 31 29 28 29 30 30 31 29 29 28 30 29 29 29 28 30 31 29 29 30 29 31 29 29 30 30 28 31 30 29 28 30 31 30 30 29 30 29 28 31 30 29 28 30 29 29 28 31 29 28 30 31 30 29 30 31 29 29 29 29 28 30 30 31 28 30 31 29 29 31 29 28 29 28 31 30 30 29 30 30 31 31 30 30 30 30 30 31], [10 10 1 1 1]);\r\ntruth = reshape([1 2 3 4 5 6 7 0 11 12 0 0 0 0 0 0 8 9 10 13 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 16 15 0 0 0 0 0 0 0 0 17 18 0 0 0 0 0 0 0 0 20 19 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 24 23 22 0 0 0 0 0 0 26 25 0 0 0 0 0 0 0 0 27 28 29 30 31], [10 10 1 1 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([20 29 22 23 23 23 22 30 29 21 28 29 23 29 29 29 30 29 29 29 23 22 31 30 31 23 23 29 23 21 29 29 23 22 31 23 23 22 22 31 28 23 21 28 29 31 23 30 31 23 30 22 31 23 23 28 30 31 29 21 31 29 31 21 29 22 31 29 29 29 23 23 23 31 23 22 31 22 31 31 28 23 28 23 23 23 22 31 30 23 28 23 23 28 23 31 30 31 23 23 28 31 29 28 29 31 29 29 31 31 29 31 30 29 31 29 30 31 30 31 30 30 30 31 31], [5 5 1 5 1]);\r\ntruth = reshape([1 2 0 0 0 0 3 0 0 0 13 14 0 0 0 12 15 16 0 0 11 18 17 0 0 0 0 0 0 0 0 4 0 0 0 0 5 0 0 0 9 8 25 28 29 10 19 26 27 30 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 7 24 0 0 0 20 23 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 22 0 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33], [5 5 1 5 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([18 23 19 28 27 22 23 22 31 19 25 22 29 25 21 27 25 29 27 29 30 30 31 22 31 29 23 29 29 29 29 21 29 21 31 31 30 31 31 29 29 29 21 29 29 23 23 23 30 31 23 23 29 27 19 27 27 31 22 31 30 23 28 23 27 22 29 29 19 25 19 30 31 27 23 30 22 30 28 31 29 31 21 23 23 30 29 29 21 29 22 31 31 31 31 31 30 31 30 23 27 23 22 23 29 17 19 29 23 29 25 23 19 27 29 31 22 30 31 29 18 31 19 27 31 23 31 21 28 29 31 23 31 31 29 30 29 29 31 29 23 29 30 28 31 23 30 31 31 23 19 27 29 23 27 31 30 31 27 25 19 27 30 30 31 22 31 21 26 27 31 19 31 22 23 31 30 31 22 29 28 31 28 31 31 31 29 23 21 21 23 30 23 31 31 23 23 22 30 31 29 26 31 30 29 27 26 30 29 29 30 30 30 31 27 29 26 31 25 25 30 31 27 31 31 30 30 28 31 29 30 31 30 30 31 28 29 30 31 31 31 31 30 31 29 30 31 30 30 31], [5 5 2 5 1]);\r\ntruth = reshape([1 2 0 0 0 0 0 0 0 0 0 24 23 0 0 0 19 22 0 0 0 20 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 13 0 0 0 17 14 0 0 0 3 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 11 0 0 0 0 12 0 0 0 16 15 0 0 0 4 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 35 36 0 6 7 0 0 0 0 0 0 0 0 28 0 0 0 0 29 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 38 0 0 0 34 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 32 39 0 0 0 0 40], [5 5 2 5 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([21 5 22 29 19 23 31 7 30 15 21 26 23 7 19 23 22 29 11 11 14 15 30 30 23 11 7 11 25 15 13 13 15 15 27 23 31 28 29 27 13 14 31 31 15 23 14 23 30 19 30 30 31 29 25 7 14 29 23 31 25 27 30 29 23 30 29 12 31 19 7 23 31 19 11 28 30 15 26 27 15 13 25 26 23 27 23 23 28 15 19 20 23 27 15 7 31 19 23 27 28 23 22 31 15 30 23 29 30 15 15 28 31 23 21 23 30 23 29 23 22 15 15 23 23 26 15 23 7 29 23 28 31 25 15 23 27 27 31 23 29 23 14 29 25 23 7 15 23 31 27 30 15 25 21 28 23 19 31 15 31 27 23 19 23 22 30 30 27 15 30 15 27 7 23 30 27 26 31 25 23 19 30 31 23 29 29 14 31 27 23 30 23 15 23 23 23 30 31 19 7 23 23 25 27 26 27 23 13 23 21 31 23 27 15 31 15 29 26 23 15 19 31 23 15 29 23 30 31 15 31 31 7 28 31 14 23 13 23 15 29 21 31 30 31 31 31 23 30 31 26 27 25 31 21 15 21 31 28 31 15 23 21 29 29 26 31 29 27 29 30 31 27 15 15 27 23 23 13 27 25 23 23 29 19 30 27 27 31 19 31 28 27 15 29 15 23 23 23 15 15 26 31 5 15 31 30 15 29 31 26 31 30 31 29 15 13 31 17 31 27 31 27 15 15 27 31 30 30 27 11 13 23 19 29 31 27 29 14 31 28 29 27 27 15 31 31 27 30 23 30 31 15 13 15 15 15 29 31 13 13 31 15 31 29 29 29 23 23 23 15 23 30 23 7 22 27 7 19 29 25 30 30 30 31 23 29 23 30 25 11 29 7 22 31 26 31 26 30 27 29 31 27 28 27 15 15 27 27 27 28 30 27 19 31 31 14 15 27 21 30 31 31 23 31 19 28 31 27 15 21 21 23 31 29 27 31 29 23 15 22 29 29 31 25 27 23 15 7 15 29 25 13 30 29 15 27 21 27 23 30 15 29 26 23 28 29 15 22 31 27 23 3 14 31 13 15 14 30 29 29 15 23 23 29 31 14 30 31 23 22 23 15 15 29 23 15 23 15 31 15 30 28 30 29 28 27 31 11 27 31 13 30 15 11 13 28 31 15 11 15 27 14 30 15 28 27 28 15 11 30 31 15 29 13 28 30 27 31 31 30 30 30 27 31 30 31 26 31 15 15 29 28 15 27 25 31 31 13 13 31 26 31 31 29 29 27 26 31 29 27 30 31 30 31 25 28 27 30 31 31 27 27 28 31 26 31 26 31 15 26 29 30 31 27 15 31 31 14 27 29 15 15 14 15 30 31 31 29 15 30 15 30 31 15 31 29 30 30 15 15 15 15 14 31 25 30 30 18 31 15 21 23 14 31 7 30 29 15 13 14 27 27 23 31 23 27 7 26 27 31 27 23 31 29 29 15 15 15 29 30 15 19 21 15 29 31 29 29 27 15 31 23 31 23 21 29 13 22 15 31 31 27 25 11 14 29 29 22 31 28 31 31 11 27 31 14 30 30 31 7 22 31 25 13 23 25 27 23 31 25 23 25 22 31 29 28 31 27 31 31 27 30 26 15 21 23 21 29 15 15 23 31 15 23 31 13 30 30 23 28 31 21 15 13 23 31 31 22 31 19 31 7 30 23 26 31 30 29 15 30 15 22 31 15 19 28 31 30 23 23 31 26 31 7 15 7 27 14 29 29 26 30 27 31 23 26 31 23 25 30 30 14 31 31 26 31 27 30 31 23 11 31 15 21 7 23 28 31 23 26 31 31 31 27 26 30 30 31 25 30 23 31 14 31 31 31 21 26 15 30 30 31 31 7 15 23 28 31 31 15 29 27 13 15 15 30 30 31 27 29 23 29 29 15 23 23 31 30 31 30 23 30 29 31 28 31 31 15 15 15 14 15 31 15 30 23 27 7 17 30 15 19 13 31 27 7 23 30 23 31 22 27 29 27 30 23 22 31 31 22 31 23 15 15 22 29 15 19 11 31 23 19 30 29 22 23 27 23 31 19 19 22 15 31 31 11 19 31 31 31 29 30 31 31 19 29 29 14 29 29 31 31 25 29 31 31 19 31 31 27 29 31 11 19 29 31 28 30 31 31 29 23 30 15 14 29 13 23 29 7 27 31 14 31 22 30 31 31 31 31 15 7 13 15 15 29 30 31 23 7 23 15 14 15 15 31 14 15 23 21 23 31 27 27 30 23 27 28 27 27 27 15 29 27 27 29 21 30 27 27 31 31 14 27 15 11 23 27 23 31 23 27 23 15 15 31 30 15 23 31 31 15 23 27 15 27 30 30 31 25 27 30 31 23 30 29 30 27 27 15 30 27 14 31 14 23 27 13 27 7 27 31 31 23 15 27 23 19 15 29 22 30 31 25 27 23 31 25 27 21 7 23 31 23 31 15 27 15 15 21 31 15 29 15 15 29 29 15 7 15 31 29 23 29 30 31 31 23 7 14 31 30 15 15 31 30 30 30 29 25 15 11 15 15 27 29 29 29 31 30 31 31 31 15 15 14 31 11 27 28 27 27 29 29 31 27 31 31 31 29 28 30 29 15 27 30 29 31 9 14 27 27 15 31 15 15 30 31 27 11 29 13 15 29 13 30 31 25 15 31 30 30 31 29 15 30 31 11 31 13 14 31 30 31 31 15 28 30 29 26 31 27 27 15 30 31 30 15 29 15 30 11 31 27 29 14 31 29 15 31 14 29 15 31 30 31 31 31 15 28 30 30 31 15 31 15 31 30 31 7 28 30 15 13 15 30 23 15 15 15 21 21 30 31 29 23 27 7 27 23 23 23 22 15 24 30 29 19 21 21 15 31 27 23 31 30 29 28 31 22 15 31 29 29 30 23 30 23 23 31 11 27 15 23 25 28 15 29 29 31 30 23 29 29 23 11 19 31 27 19 27 19 26 27 23 23 29 7 23 23 7 30 29 9 30 11 15 23 15 25 27 30 15 15 31 27 15 27 31 14 30 29 14 31 29 13 29 29 31 23 31 15 23 7 30 31 21 23 15 23 15 30 31 15 30 27 30 15 19 22 27 30 28 31 13 31 29 30 31 29 27 30 31 13 31 30 27 23 31 27 31 21 31 31 27 15 27 14 31 22 15 11 25 19 30 31 27 30 23 19 23 30 31 15 23 29 26 31 29 15 27 30 29 15 28 27 31 31 27 23 21 14 30 29 31 23 23 23 15 29 30 31 27 31 31 31 19 26 31 15 15 28 31 23 30 29 23 29 23 30 31 26 31 23 22 29 29 29 29 28 31 29 31 15 31 22 31 30 29 14 30 31 31 31 31 23 31 14 31 11 15 11 23 29 30 31 22 31 23 29 29 19 24 27 26 31 22 31 13 26 15 14 31 31 30 29 31 23 11 13 30 31 14 31 31 7 23 31 31 14 31 11 25 27 31 15 23 31 19 27 15 7 13 19 28 15 25 15 7 30 27 27 27 15 30 31 15 23 15 14 31 27 30 31 27 19 29 29 31 21 7 31 30 23 30 31 27 27 31 30 29 31 13 15 27 15 27 31 7 31 15 7 22 29 7 29 15 15 31 23 31 29 15 13 15 30 15 23 31 31 23 31 30 23 29 11 7 30 27 11 27 27 25 15 28 23 31 15 19 15 28 31 11 13 11 30 23 30 31 27 31 27 30 31 11 7 29 29 23 29 15 31 31 29 29 22 31 23 15 15 14 11 19 23 30 23 30 27 23 31 19 22 15 29 23 21 21 15 29 30 15 15 31 29 27 30 31 30 31 23 30 29 15 15 29 31 29 21 15 30 23 15 27 11 21 27 22 30 25 31 23 7 23 31 14 23 31 15 29 15 14 23 23 15 31 23 14 31 23 7 23 7 15 31 15 15 30 15 31 13 11 27 13 13 30 31 29 31 31 14 31 15 11 27 28 30 28 31 15 31 15 31 30 27 25 29 30 29 27 15 31 14 31 15 28 30 30 31 31 27 29 27 15 31 15 30 27 31 31 31 31 26 29 29 30 31 31 31 15 31 31 27 26 31 27 25 30 27 11 30 31 15 30 15 31 31 30 30 15 30 30 15 30 27 14 31 30 31 31 31 29 14 31 15 15 31 31 27 27 13 31 28 15 15 30 31 31 31 15 14 31 13 13 31 29 15 31 15 29 30 31 14 31 31 30 27 21 30 31 30 29 27 30 31 23 29 28 15 21 21 15 30 31 29 15 22 15 11 31 25 27 11 30 25 27 27 15 13 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30 23 23 22 31 27 15 27 15 21 28 31 7 15 15 15 23 13 31 13 25 23 31 23 31 22 30 23 30 27 31 11 15 15 31 30 15 25 14 31 22 31 27 26 29 23 31 27 15 31 23 11 23 22 31 21 15 19 29 14 31 23 23 23 7 15 29 15 23 29 30 31 27 14 31 15 23 23 28 30 23 29 31 29 19 27 29 15 23 25 29 31 15 29 15 27 14 31 30 30 31 31 7 30 30 30 31 7 29 31 29 13 29 23 29 31 23 23 15 29 31 31 7 23 31 7 31 15 31 15 26 31 15 13 29 13 30 31 31 29 31 31 13 29 30 30 31 31 15 27 30 27 31 11 28 31 28 15 27 29 27 29 15 13 15 27 15 13 29 25 15 30 31 31 31 15 15 15 31 25 30 15 15 29 30 31 29 31 31 29 27 27 25 25 31 28 27 31 14 31 31 27 31 28 31 15 13 29 27 15 30 31 15 31 15 30 31 15 15 13 11 27 15 30 31 14 31 15 29 28 29 29 31 31 15 31 15 15 31 14 31 31 31 28 31 31 31 29 31 15 31 14 31 30 18 22 30 31 26 23 22 30 25 22 29 26 31 31 19 31 28 30 23 26 31 31 27 23 25 29 23 22 27 31 31 23 27 31 30 30 31 27 29 23 21 22 27 27 22 30 23 30 31 27 26 29 26 31 29 25 23 30 27 29 29 29 27 23 27 23 31 23 23 30 31 26 30 31 29 23 28 31 19 27 23 29 23 23 26 23 27 30 23 28 27 30 27 27 23 23 30 27 23 22 31 31 30 31 23 31 22 29 29 29 23 29 29 29 31 31 23 31 23 30 30 31 31 23 21 31 29 28 23 29 27 31 27 27 31 25 25 22 27 29 23 30 29 23 30 23 19 31 27 29 25 23 21 27 27 31 23 31 23 21 29 23 23 23 31 31 23 30 23 27 27 31 22 23 25 29 27 26 31 31 27 30 31 23 21 22 23 26 31 23 27 22 31 23 31 30 27 26 27 27 31 30 31 23 23 31 28 31 27 19 31 21 31 29 25 27 31 21 31 30 31 31 31 31 31 30 31 22 23 29 21 30 29 31 31 31 23 29 23 31 29 29 29 23 22 31 30 31 31 27 29 30 29 21 23 29 29 31 23 28 31 29 23 23 29 23 29 19 23 23 23 30 31 23 29 22 31 27 19 29 25 29 28 31 31 29 31 31 21 21 23 30 31 31 31 26 23 31 27 23 19 30 31 31 28 31 22 27 19 31 29 29 21 25 29 31 31 31 31 30 31 27 26 31 23 30 30 23 29 18 31 25 31 31 30 31 31 23 23 29 29 22 31 21 27 30 30 31 19 22 23 23 31 31 31 29 23 29 23 30 31 31 30 21 30 30 30 31 31 31 23 30 22 31 26 30 29 27 31 31 28 31 29 31 21 31 29 27 31 29 23 30 31 23 31 23 26 23 23 27 27 19 31 31 23 26 30 31 25 23 25 19 27 31 27 31 26 23 25 23 19 31 30 31 31 31 23 29 29 21 31 23 27 31 27 31 31 21 29 27 29 29 23 31 27 27 31 30 19 29 26 29 30 23 31 25 30 31 27 29 27 19 23 31 31 29 30 31 31 23 31 22 23 31 23 31 30 30 23 28 31 29 29 31 23 23 31 29 29 23 23 28 31 31 22 31 31 30 31 30 27 30 27 27 30 29 29 26 29 29 27 30 29 31 29 27 28 31 29 31 27 30 31 31 29 30 30 31 31 31 26 30 31 29 31 31 30 31 31 28 31 27 30 29 31 31 27 27 31 28 30 31 27 27 31 30 31 29 25 25 26 30 31 27 30 27 30 31 29 30 27 30 31 31 26 30 31 31 31 28 31 27 25 31 31 31 27 31 31 31 31 31 25 27 27 27 31 31 27 31 28 28 30 31 30 31 29 31 29 29 31 31 29 29 31 29 28 31 31 31 30 31 30 31], [5 5 5 5 5]);\r\ntruth = reshape([1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 95 94 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 98 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 96 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 6 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 38 37 0 0 40 39 122 123], [5 5 5 5 5]);\r\nassert(isequal(solve_maze5(maze), truth));","published":true,"deleted":false,"likes_count":5,"comments_count":19,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":48,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":35,"created_at":"2017-10-12T15:00:57.000Z","updated_at":"2026-03-19T20:06:56.000Z","published_at":"2017-10-16T01:51:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is a generalization of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/283\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 283\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. If you haven't solved that yet, I would recommend solving it first.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEncoding\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maze will be represented by an [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eO\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eQ\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ] matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWalls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe start position is at the origin: subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(1,1,1,1,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe end position is at the furthest extent: subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(M,N,O,P,Q)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/283\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 283\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for a 2-D example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2646,"title":"Determine the number of maximal cliques in an undirected graph","description":"In an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A _maximal_ clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\r\n\r\nGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\r\n\r\n*Example*\r\n\r\nConsider the graph shown below,\r\n\r\n\u003c\u003chttps://dl.dropboxusercontent.com/u/13345152/examplegraph.png\u003e\u003e\r\n\r\nwhich has the following adjacency matrix:\r\n\r\n A = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\r\nThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\r\n\r\nNOTE: You may assume the data type of the adjacency matrix is double.","description_html":"\u003cp\u003eIn an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A \u003ci\u003emaximal\u003c/i\u003e clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\u003c/p\u003e\u003cp\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsider the graph shown below,\u003c/p\u003e\u003cimg src = \"https://dl.dropboxusercontent.com/u/13345152/examplegraph.png\"\u003e\u003cp\u003ewhich has the following adjacency matrix:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\u003c/pre\u003e\u003cp\u003eThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\u003c/p\u003e\u003cp\u003eNOTE: You may assume the data type of the adjacency matrix is double.\u003c/p\u003e","function_template":"function num = maximalcliques(A)\r\n num = 0;\r\nend","test_suite":"%%\r\nA = 0;\r\nassert(isequal(maximalcliques(A),1))\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = ones(N)-eye(N);\r\n assert(isequal(maximalcliques(A),1))\r\nend\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = zeros(N);\r\n assert(isequal(maximalcliques(A),N))\r\nend\r\n\r\n%%\r\nA = [ 0 0 0 0\r\n 0 0 0 1\r\n 0 0 0 0\r\n 0 1 0 0 ];\r\nassert(isequal(maximalcliques(A),3))\r\n\r\n%%\r\nA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ];\r\nassert(isequal(maximalcliques(A),3))\r\n\r\n%%\r\nA = [ 0 1 1 0 0 0\r\n 1 0 1 1 0 0\r\n 1 1 0 0 1 0\r\n 0 1 0 0 1 0\r\n 0 0 1 1 0 1\r\n 0 0 0 0 1 0 ];\r\nassert(isequal(maximalcliques(A),5))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":"2014-10-28T21:35:49.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-10-28T21:07:46.000Z","updated_at":"2014-10-28T22:05:37.000Z","published_at":"2014-10-28T21:35:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emaximal\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the graph shown below,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich has the following adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [ 0 1 0 0 0\\n 1 0 1 1 0\\n 0 1 0 1 0\\n 0 1 1 0 1\\n 0 0 0 1 0 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: You may assume the data type of the adjacency matrix is double.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":2647,"title":"Find the maximal cliques in an undirected graph","description":"This is a variant of a \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph previous problem\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\r\n\r\nGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\r\n\r\n*Example*\r\n\r\nConsider the graph shown below,\r\n\r\n\u003c\u003chttps://dl.dropboxusercontent.com/u/13345152/examplegraph.png\u003e\u003e\r\n\r\nwhich has the following adjacency matrix:\r\n\r\n A = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\r\nThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\r\n\r\n C = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ]\r\n\r\nNOTE: You may assume the data type of the adjacency matrix (A) is double.","description_html":"\u003cp\u003eThis is a variant of a \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph\"\u003eprevious problem\u003c/a\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\u003c/p\u003e\u003cp\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsider the graph shown below,\u003c/p\u003e\u003cimg src = \"https://dl.dropboxusercontent.com/u/13345152/examplegraph.png\"\u003e\u003cp\u003ewhich has the following adjacency matrix:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\u003c/pre\u003e\u003cp\u003eThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eC = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ]\r\n\u003c/pre\u003e\u003cp\u003eNOTE: You may assume the data type of the adjacency matrix (A) is double.\u003c/p\u003e","function_template":"function C = maximalcliques(A)\r\n C = [];\r\nend","test_suite":"%%\r\nA = 0;\r\nassert(isequal(maximalcliques(A),1))\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = ones(N)-eye(N);\r\n assert(isequal(maximalcliques(A),ones(N,1)))\r\nend\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = zeros(N);\r\n C = maximalcliques(A);\r\n assert(isequal(fliplr(sortrows(C')'),eye(N)))\r\nend\r\n\r\n%%\r\nA = [ 0 0 0 0\r\n 0 0 0 1\r\n 0 0 0 0\r\n 0 1 0 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0\r\n 0 1 0\r\n 0 0 1\r\n 0 1 0 ];\r\nassert(isequal(C,C_correct))\r\n\r\n%%\r\nA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ];\r\nassert(isequal(C,C_correct))\r\n\r\n%%\r\nA = [ 0 1 1 0 0 0\r\n 1 0 1 1 0 0\r\n 1 1 0 0 1 0\r\n 0 1 0 0 1 0\r\n 0 0 1 1 0 1\r\n 0 0 0 0 1 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0 0 0\r\n 1 1 0 0 0\r\n 1 0 1 0 0\r\n 0 1 0 1 0\r\n 0 0 1 1 1\r\n 0 0 0 0 1 ];\r\nassert(isequal(C,C_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2014-10-28T22:05:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-10-28T21:53:54.000Z","updated_at":"2014-10-28T22:05:02.000Z","published_at":"2014-10-28T22:05:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a variant of a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eprevious problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the graph shown below,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich has the following adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [ 0 1 0 0 0\\n 1 0 1 1 0\\n 0 1 0 1 0\\n 0 1 1 0 1\\n 0 0 0 1 0 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = [ 1 0 0\\n 1 1 0\\n 0 1 0\\n 0 1 1\\n 0 0 1 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: You may assume the data type of the adjacency matrix (A) is double.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":1886,"title":"Graceful Double Wheel Graph","description":"\u003chttp://en.wikipedia.org/wiki/Graceful_labeling Graceful Graphs\u003e are the topic of the \u003chttp://www.azspcs.net/Contest/GracefulGraphs Primes Graceful Graph Contest\u003e , 21 September 2013 thru 21 December 2013.\r\n\r\nThis Challenge is to create \u003chttp://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html Graceful Double Wheel Graphs\u003e for various N. A \u003chttp://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf General Algorithm by Le Bras of Cornell\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon \u003chttp://oeis.org/A004137 OEIS A004137\u003e.\r\n\r\n*Example:*\r\nOne solution for N=11:\r\n\r\n\u003c\u003chttp://www.comp.leeds.ac.uk/bms/Graceful/Images/2C5+K1.gif\u003e\u003e\r\n\r\nwhich could be answered as [1 3 14 6 19;20 5 17 7 16].\r\n\r\nThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\r\n\r\n*Input:* N [Total number of Nodes (odd) and N\u003e10 ]\r\n\r\n*Output:* M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]","description_html":"\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Graceful_labeling\"\u003eGraceful Graphs\u003c/a\u003e are the topic of the \u003ca href = \"http://www.azspcs.net/Contest/GracefulGraphs\"\u003ePrimes Graceful Graph Contest\u003c/a\u003e , 21 September 2013 thru 21 December 2013.\u003c/p\u003e\u003cp\u003eThis Challenge is to create \u003ca href = \"http://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html\"\u003eGraceful Double Wheel Graphs\u003c/a\u003e for various N. A \u003ca href = \"http://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf\"\u003eGeneral Algorithm by Le Bras of Cornell\u003c/a\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon \u003ca href = \"http://oeis.org/A004137\"\u003eOEIS A004137\u003c/a\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\r\nOne solution for N=11:\u003c/p\u003e\u003cimg src = \"http://www.comp.leeds.ac.uk/bms/Graceful/Images/2C5+K1.gif\"\u003e\u003cp\u003ewhich could be answered as [1 3 14 6 19;20 5 17 7 16].\u003c/p\u003e\u003cp\u003eThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e N [Total number of Nodes (odd) and N\u003e10 ]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]\u003c/p\u003e","function_template":"function m=double_wheel(n)\r\n m=[];\r\nend","test_suite":"%%\r\ntic\r\nn=11;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=13;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=17;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=19;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=71;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=97;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-21T23:15:03.000Z","updated_at":"2013-09-22T01:16:42.000Z","published_at":"2013-09-22T01:16:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Graceful_labeling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGraceful Graphs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are the topic of the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.azspcs.net/Contest/GracefulGraphs\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePrimes Graceful Graph Contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e , 21 September 2013 thru 21 December 2013.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to create\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGraceful Double Wheel Graphs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for various N. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGeneral Algorithm by Le Bras of Cornell\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://oeis.org/A004137\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOEIS A004137\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e One solution for N=11:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich could be answered as [1 3 14 6 19;20 5 17 7 16].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N [Total number of Nodes (odd) and N\u0026gt;10 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,<!DOCTYPE html>
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Saturday 10 October 2020, 10:00 - 16:00                                            </p>

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\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":58389,"title":"Minimum number of crossings in a complete graph","description":"This problem is related to problem 58384.\r\nA complete graph may be drawn in different ways, such that the number of line crossings differs between drawings.\r\nTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\r\nNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\r\nGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\r\nExample:\r\nn = 6;\r\nb = 3","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 242.898px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 121.449px; transform-origin: 406.996px 121.449px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is related to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/58384-minimum-number-of-crossings-in-a-complete-bipartite-graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 58384\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecomplete graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8807px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 20.4403px; transform-origin: 403.999px 20.4403px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003en = 6;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eb = 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b = your_fcn_name(n)\r\n b = n;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext,'regexp')))\r\nassert(isempty(strfind(filetext,'assign')))\r\nassert(isempty(strfind(filetext,'eval')))\r\nassert(isempty(strfind(filetext,'echo')))\r\nassert(isempty(strfind(filetext,'!')))\r\n\r\n%%\r\nn = 6;\r\nassert(isequal(your_fcn_name(n),3))\r\n\r\n%%\r\nn = 12;\r\nassert(isequal(your_fcn_name(n),150))\r\n\r\n%%\r\nn = 35;\r\nassert(isequal(your_fcn_name(n),18496))\r\n\r\n%%\r\nn = 84;\r\nassert(isequal(your_fcn_name(n),706020))\r\n\r\n%%\r\nn = 99;\r\nassert(isequal(your_fcn_name(n),1382976))\r\n\r\n%%\r\nn = 200;\r\nassert(isequal(your_fcn_name(n),24012450))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":15521,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-06-02T18:43:47.000Z","updated_at":"2026-03-03T22:13:51.000Z","published_at":"2023-06-02T18:43:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/58384-minimum-number-of-crossings-in-a-complete-bipartite-graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 58384\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecomplete graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of vertices in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the number of vertices, n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[n = 6;\\nb = 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58384,"title":"Minimum number of crossings in a complete bipartite graph","description":"This problem is related to problem 58389.\r\nA complete bipartite graph may be drawn in different ways, such that the number of line crossings differs between drawings.\r\nTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\r\nNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\r\nGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\r\nExample:\r\nm = 3;\r\nn = 4;\r\nb = 2","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 284.347px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 142.173px; transform-origin: 406.996px 142.173px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is related to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/58389\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eproblem 58389\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_bipartite_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ecomplete bipartite graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.321px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 30.6534px; transform-origin: 403.999px 30.6605px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em = 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003en = 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; transform-origin: 403.999px 10.2202px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eb = 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b = your_fcn_name(m,n)\r\n b = m * n;\r\nend","test_suite":"%%\r\nfiletext = fileread('your_fcn_name.m');\r\nassert(isempty(strfind(filetext,'regexp')))\r\nassert(isempty(strfind(filetext,'assign')))\r\nassert(isempty(strfind(filetext,'eval')))\r\nassert(isempty(strfind(filetext,'echo')))\r\nassert(isempty(strfind(filetext,'!')))\r\n\r\n%%\r\n[m,n] = deal(3,4)\r\nassert(isequal(your_fcn_name(m,n),2))\r\n\r\n%%\r\n[m,n] = deal(4,3)\r\nassert(isequal(your_fcn_name(m,n),2))\r\n\r\n%%\r\n[m,n] = deal(12,15)\r\nassert(isequal(your_fcn_name(m,n),1470))\r\n\r\n%%\r\n[m,n] = deal(43,19)\r\nassert(isequal(your_fcn_name(m,n),35721))\r\n\r\n%%\r\n[m,n] = deal(108,8)\r\nassert(isequal(your_fcn_name(m,n),34344))\r\n\r\n%%\r\n[m,n] = deal(79,85)\r\nassert(isequal(your_fcn_name(m,n),2683044))\r\n\r\n%%\r\n[m,n] = deal(435,187)\r\nassert(isequal(your_fcn_name(m,n),407272761))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":15521,"edited_by":15521,"edited_at":"2023-06-02T18:45:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-06-02T18:28:39.000Z","updated_at":"2023-06-02T18:45:28.000Z","published_at":"2023-06-02T18:28:39.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is related to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/58389\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eproblem 58389\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_bipartite_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ecomplete bipartite graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be drawn in different ways, such that the number of line crossings differs between drawings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo date, the question of how to draw the graph so as to minimize the number of crossings remains unsolved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNevertheless, an upper bound can be calculated for the number of crossings, based on the number of elements in each of the two sets comprising the graph's vertices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the number of elements in each set, m and n, calculate the upper bound, b, for the minimum number of line crossings in the resulting complete bipartite graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[m = 3;\\nn = 4;\\nb = 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1170,"title":"Count the Number of Undirected Cycles in a Graph","description":"Given a symmetric adjacency matrix, determine the number of unique undirected cycles.\r\nFor example, the graph represented by adjacency matrix\r\nA = [\r\n 0 1 1 0 1\r\n 1 0 1 1 0\r\n 1 1 0 1 1\r\n 0 1 1 0 0\r\n 1 0 1 0 0];\r\nhas 6 cycles. They are:\r\n[1 -\u003e 2 -\u003e 3 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 5 -\u003e 1]\r\n[2 -\u003e 3 -\u003e 4 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 1]\r\n[1 -\u003e 2 -\u003e 3 -\u003e 5 -\u003e 1]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 5 -\u003e 1]\r\nThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 399.2px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 199.6px; transform-origin: 407px 199.6px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 217.5px 8px; transform-origin: 217.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a symmetric adjacency matrix, determine the number of unique\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eundirected\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e cycles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the graph represented by adjacency matrix\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 1 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehas 6 cycles. They are:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 112px 8.5px; tab-size: 4; transform-origin: 112px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 3 -\u0026gt; 5 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381px 8px; transform-origin: 381px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nCycles = count_undirected_cycles(A)\r\n nCycles = 0;\r\nend","test_suite":"%%\r\nA = [\r\n 0 1 1 0 1\r\n 1 0 1 1 0\r\n 1 1 0 1 1\r\n 0 1 1 0 0\r\n 1 0 1 0 0];\r\nnCycles = 6;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 1 1 1 0 1 0\r\n 0 0 0 1 1 1 1 1\r\n 1 0 0 0 1 0 1 0\r\n 1 1 0 0 1 0 0 1\r\n 1 1 1 1 0 0 0 1\r\n 0 1 0 0 0 0 0 1\r\n 1 1 1 0 0 0 0 1\r\n 0 1 0 1 1 1 1 0];\r\nnCycles = 136;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 0 0 1 0 1 1\r\n 0 0 1 1 1 1 0 0 1 0\r\n 0 1 0 1 0 1 1 0 0 1\r\n 0 1 1 0 0 0 0 1 0 0\r\n 0 1 0 0 0 0 0 1 1 1\r\n 0 1 1 0 0 0 0 1 1 0\r\n 1 0 1 0 0 0 0 0 0 0\r\n 0 0 0 1 1 1 0 0 0 0\r\n 1 1 0 0 1 1 0 0 0 1\r\n 1 0 1 0 1 0 0 0 1 0];\r\nnCycles = 251;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 0 1 1 0 0 1\r\n 0 0 1 1 0 0 1 1 0 0\r\n 0 1 0 0 0 1 1 1 0 1\r\n 0 1 0 0 1 0 1 0 1 1\r\n 0 0 0 1 0 0 0 0 1 1\r\n 1 0 1 0 0 0 1 1 1 1\r\n 1 1 1 1 0 1 0 0 0 0\r\n 0 1 1 0 0 1 0 0 1 1\r\n 0 0 0 1 1 1 0 1 0 1\r\n 1 0 1 1 1 1 0 1 1 0];\r\nnCycles = 1579;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 1 1 1 1 0 0\r\n 0 0 0 0 0 1 1 1\r\n 1 0 0 0 1 0 1 0\r\n 1 0 0 0 1 1 0 1\r\n 1 0 1 1 0 1 0 1\r\n 1 1 0 1 1 0 0 1\r\n 0 1 1 0 0 0 0 1\r\n 0 1 0 1 1 1 1 0];\r\nnCycles = 147;\r\nassert(isequal(count_undirected_cycles(A),nCycles))\r\n\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":4,"created_by":1057,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2021-12-31T15:55:01.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T13:44:54.000Z","updated_at":"2025-11-22T15:47:53.000Z","published_at":"2013-01-04T13:48:29.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a symmetric adjacency matrix, determine the number of unique\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eundirected\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cycles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the graph represented by adjacency matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [\\n 0 1 1 0 1\\n 1 0 1 1 0\\n 1 1 0 1 1\\n 0 1 1 0 0\\n 1 0 1 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehas 6 cycles. They are:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[1 -\u003e 2 -\u003e 3 -\u003e 1]\\n[1 -\u003e 3 -\u003e 5 -\u003e 1]\\n[2 -\u003e 3 -\u003e 4 -\u003e 2]\\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 1]\\n[1 -\u003e 2 -\u003e 3 -\u003e 5 -\u003e 1]\\n[1 -\u003e 2 -\u003e 4 -\u003e 3 -\u003e 5 -\u003e 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) undirected cycles in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58618,"title":"Cycle Detection","description":"Detect if a graph has cycles:\r\nInputs\r\nn: the number of vertices (where each vertex corresponds to an integer from 1 to n)\r\nedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\r\nReturn:\r\ntrue if the graph has cycles and false otherwise","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 171px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 85.5px; transform-origin: 407px 85.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.475px 7.5px; transform-origin: 87.475px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDetect if a graph has cycles:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.0917px 7.5px; transform-origin: 19.0917px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInputs\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.85px 7.5px; transform-origin: 256.85px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003en: the number of vertices (where each vertex corresponds to an integer from 1 to n)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 249.033px 7.5px; transform-origin: 249.033px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 22.7917px 7.5px; transform-origin: 22.7917px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 145.608px 7.5px; transform-origin: 145.608px 7.5px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003etrue if the graph has cycles and false otherwise\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = has_cycle(n, edges)\r\n y = x;\r\nend","test_suite":"%%\r\nn = 5;\r\nedges = [1 3; 2 3; 3 4; 4 5];\r\ny_correct = false;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n\r\n%%\r\nn = 3;\r\nedges = [];\r\ny_correct = false;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n\r\n%%\r\nn = 6;\r\nedges = [1 3; 2 3; 3 4; 4 5; 5 6; 6 3];\r\ny_correct = true;\r\nassert(isequal(has_cycle(n, edges),y_correct));\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2456395,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-18T11:27:52.000Z","updated_at":"2023-07-18T11:27:52.000Z","published_at":"2023-07-18T11:27:52.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetect if a graph has cycles:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInputs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en: the number of vertices (where each vertex corresponds to an integer from 1 to n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eedges: the list of edges (in the form of pairs (i, j) where i and j represent vertices)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003etrue if the graph has cycles and false otherwise\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":475,"title":"Is this group simply connected?","description":"Given connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\r\n\r\nExample 1:\r\n \r\n Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\r\n \r\nThe three nodes of this graph are fully connected, since this graph could be drawn like so:\r\n\r\n 3--8--9\r\n\r\nExample 2:\r\n\r\n Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\r\n\r\nThis graph could be drawn like so:\r\n\r\n 1--2 5--6\r\n | |\r\n 4--3\r\n\r\nThere are two distinct subgraphs.","description_html":"\u003cp\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\u003c/pre\u003e\u003cp\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 3--8--9\u003c/pre\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\u003c/pre\u003e\u003cp\u003eThis graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 1--2 5--6\r\n | |\r\n 4--3\u003c/pre\u003e\u003cp\u003eThere are two distinct subgraphs.\u003c/p\u003e","function_template":"function tf = isConnected(node_pairs)\r\n tf = true;\r\nend","test_suite":"%%\r\n\r\nnode_pairs = [8 9; 8 3];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6 ];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6;\r\n 6 2 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 2 100];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 50 100];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 4 17 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":2,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":36,"test_suite_updated_at":"2012-03-09T22:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-09T22:05:26.000Z","updated_at":"2026-03-03T23:05:27.000Z","published_at":"2012-03-12T16:23:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 8 9\\n 8 3 ]\\n Output tf is true]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 3--8--9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 1 2 \\n 2 3\\n 1 4\\n 3 4\\n 5 6 ]\\n Output tf is false]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1--2 5--6\\n | |\\n 4--3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are two distinct subgraphs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61156,"title":"How Long is the Border Between Unitopia and Zerostan?","description":"Two countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\r\nYou are the surveyor contracted to resolve this problem once and for all.\r\nThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\r\nThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\r\nIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\r\nExample 1\r\nSingle cell surrounded by zeros:\r\nInput:\r\n [0 0 0\r\n 0 1 0\r\n 0 0 0]\r\nOutput: 4\r\nThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\r\nExample 2\r\n\r\nMatrix with other values:\r\nInput:\r\n [0 0 0 0\r\n 1 1 0 0\r\n 2 2 2 2]\r\nOutput: 3\r\nOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are ignored.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1008.33px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 333.5px 504.167px; transform-origin: 333.5px 504.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTwo countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou are the surveyor contracted to resolve this problem once and for all.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 31.5px; text-align: left; transform-origin: 309.5px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSingle cell surrounded by zeros:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e [0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 0 1 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 0 0 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eOutput: 4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 224.667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 112.333px; text-align: left; transform-origin: 309.5px 112.333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.5px; text-align: left; transform-origin: 309.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMatrix with other values:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e [0 0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 1 1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e 2 2 2 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 10.8333px; text-align: left; transform-origin: 309.5px 10.8333px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eOutput: 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 309.5px 21px; text-align: left; transform-origin: 309.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are ignored.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function borderLength = calculateBorder(map)\r\n borderLength = 0;\r\nend","test_suite":"%% Test 1: Single cell surrounded by zeros\r\nmap = [0 0 0;\r\n 0 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 4))\r\n\r\n%% Test 2: Two adjacent cells\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 3: Matrix with other values\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 2 2 2];\r\nassert(isequal(calculateBorder(map), 3))\r\n\r\n%% Test 4: No border - only 1s\r\nmap = [1 1 1;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 5: No border - only 0s\r\nmap = [0 0 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 6: No border - no 1s or 0s\r\nmap = [2 3 4;\r\n 5 6 7];\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 7: Single element - 1\r\nmap = 1;\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 8: Single element - 0\r\nmap = 0;\r\nassert(isequal(calculateBorder(map), 0))\r\n\r\n%% Test 9: L-shaped region of 1s\r\nmap = [0 0 0 0;\r\n 0 1 1 0;\r\n 0 0 1 0;\r\n 0 0 1 1];\r\nassert(isequal(calculateBorder(map), 9))\r\n\r\n%% Test 10: Large block of 1s surrounded by 0s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 1 1 1 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 10))\r\n\r\n%% Test 11: T-shaped region of 1s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 0 1 0 0;\r\n 0 0 1 0 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 12))\r\n\r\n%% Test 12: Rectangular block of 1s\r\nmap = [0 0 0;\r\n 1 1 0;\r\n 1 1 0;\r\n 0 0 0];\r\nassert(isequal(calculateBorder(map), 6))\r\n\r\n%% Test 13: Horizontal bar of 1s\r\nmap = [0 0 0 0 0;\r\n 0 1 1 1 0;\r\n 0 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 14: C-shaped region\r\nmap = [1 1 1;\r\n 1 0 0;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 15: Nested rectangles\r\nmap = [1 1 1 1;\r\n 1 0 0 1;\r\n 1 0 0 1;\r\n 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 16: U-shaped region of 1s\r\nmap = [1 0 1;\r\n 1 0 1;\r\n 1 1 1];\r\nassert(isequal(calculateBorder(map), 5))\r\n\r\n%% Test 17: Mixed values with connected regions\r\nmap = [0 0 2 2;\r\n 0 1 1 2;\r\n 0 0 1 2];\r\nassert(isequal(calculateBorder(map), 4))\r\n\r\n%% Test 18: Spiral pattern\r\nmap = [1 1 1 1;\r\n 0 0 0 1;\r\n 1 1 0 1;\r\n 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 9))\r\n\r\n%% Test 19: Step pattern\r\nmap = [0 0 0 0;\r\n 1 1 0 0;\r\n 1 1 1 0;\r\n 0 0 0 0];\r\nassert(isequal(calculateBorder(map), 8))\r\n\r\n%% Test 20: Large hollow square\r\nmap = [1 1 1 1 1;\r\n 1 0 0 0 1;\r\n 1 0 0 0 1;\r\n 1 0 0 0 1;\r\n 1 1 1 1 1];\r\nassert(isequal(calculateBorder(map), 12))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":7,"edited_by":7,"edited_at":"2026-01-08T17:18:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-08T17:06:36.000Z","updated_at":"2026-03-06T19:37:51.000Z","published_at":"2026-01-08T17:18:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo countries, Unitopia (denoted by ones) and Zerostan (denoted by zeros) are engaged in a long-standing dispute: how long is the border between their two domains?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are the surveyor contracted to resolve this problem once and for all.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe border between the two countries is the sum of all the line segments that separate a 1 from a 0. Only horizontal and vertical adjacencies count (4-connected neighbors). The matrix edges do not count as borders - only internal segments between 1s and 0s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input map will be a rectangular matrix of integers. Not all integers will be 1s and 0s, but the border you are interested in is only between 1s and 0s. Other values are ignored and do not contribute to the border length.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn every case, each country will be a 4-connected region. That is, you can make a tour of every element in a given country (all 1s or all 0s) without crossing an international boundary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSingle cell surrounded by zeros:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e [0 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 0 1 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput: 4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe 1 has four neighbors (up, down, left, right), all are 0s, so border length = 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"219\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"300\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMatrix with other values:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e [0 0 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 1 1 0 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e 2 2 2 2]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput: 3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOnly 1-to-0 adjacencies (shown in red) count. The two 1s touch 0s: right 1 has 2 border segments with 0s, left 1 has 1 border segment. Total = 3. The 2s are 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2461,"title":"Graph Algorithms - 2 : Chromatic Number","description":"Chromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\r\n","description_html":"\u003cp\u003eChromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\u003c/p\u003e","function_template":"function y = gColor(x)\r\n\r\nend","test_suite":"%%\r\nx = [0 1 1 0; 1 0 1 1 ; 1 1 0 0 ; 0 1 0 0];\r\ny_correct = 3;\r\nassert(isequal(gColor(x),y_correct))\r\n\r\n%%\r\nx = [0 1 0 0 1 0 1;1 0 1 0 0 0 1; 0 1 0 1 0 0 1; 0 0 1 0 0 1 1 ; 1 0 0 0 0 0 1 ; 0 0 0 1 0 0 1; 1 1 1 1 1 1 0];\r\ny_correct = 3;\r\nassert(isequal(gColor(x),y_correct))\r\n\r\n%%\r\nx = fliplr(eye(2));\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\n%%\r\nx = [0 1 1 0 1;1 0 0 1 0;1 0 0 1 0;0 1 1 0 1;1 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\n%%\r\nx = [0 1 1 0 1;1 0 0 1 0;1 0 0 1 0;0 1 1 0 1;1 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(gColor(x),y_correct));\r\n\r\nx = zeros(2); % two vertices, not connected by any edge\r\ny_correct = 1;\r\nassert(isequal(gColor(x),y_correct));","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2014-07-25T14:47:23.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-23T13:48:08.000Z","updated_at":"2014-07-25T14:47:23.000Z","published_at":"2014-07-23T13:48:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChromatic number of a graph is the minimum number of distinct colors required to color all the vertices of the graph such that no two adjacent vertices (vertices that are connected by an edge) have same color. Adjacency matrix of a graph is given. Determine its chromatic number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2458,"title":"Graph Algorithms - 1 : Length of the largest closed path","description":"An undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\r\n\r\nDefinition of adjacency matrix : \u003chttp://en.wikipedia.org/wiki/Adjacency_matrix\u003e","description_html":"\u003cp\u003eAn undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\u003c/p\u003e\u003cp\u003eDefinition of adjacency matrix : \u003ca href = \"http://en.wikipedia.org/wiki/Adjacency_matrix\"\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/a\u003e\u003c/p\u003e","function_template":"function y = lCycle(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 0 1 0 0 0; 0 0 0 1 0 1; 1 0 0 0 1 0;0 1 0 0 0 1; 0 0 1 0 0 0; 0 1 0 1 0 0];\r\ny_correct = 3;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n%%\r\nx = [0 1 0;1 0 1;0 1 0];\r\ny_correct = 2;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = ones(5).*(~eye(5));\r\ny_correct = 5;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = ones(10).*(~eye(10));\r\ny_correct = 10;\r\nassert(isequal(lCycle(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 1 0 0 0 1];\r\nfor i = 1:length(x)\r\n a(i,:) = circshift(x,[0 (i-1)]);\r\nend\r\ny_correct = 6;\r\nassert(isequal(lCycle(a),y_correct))\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2015-01-27T13:28:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-23T10:28:15.000Z","updated_at":"2024-11-02T02:04:14.000Z","published_at":"2014-07-23T10:28:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn undirected simple graph is given as the form of an adjacency matrix. Find the length of the largest closed path (one that starts and ends in same vertex). Here, length is defined as the number of the vertices included in the path. Assume that the solution is unique.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDefinition of adjacency matrix :\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Adjacency_matrix\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2730,"title":"Graph Algorithms 3: Number of Connected Components","description":"Given an adjacency matrix of a simple undirected graph, find the number of connected components.","description_html":"\u003cp\u003eGiven an adjacency matrix of a simple undirected graph, find the number of connected components.\u003c/p\u003e","function_template":"function y = cComponents(x)\r\n y = x;\r\nend","test_suite":"%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\nmat = [a b; b a];\r\nn = 2;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\na = randi(20);\r\nmat = ones(a) - eye(a);\r\nn = 1;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\nmat = [0 1 0 0 1;\r\n 1 0 1 0 0;\r\n 0 1 0 1 0;\r\n 0 0 1 0 1;\r\n 1 0 0 1 0];\r\nassert(isequal(cComponents(mat),1))\r\n\r\n\r\n%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\nmat = [a b b; b a b; b b a];\r\nn = 3;\r\nassert(isequal(cComponents(mat),n))\r\n\r\n\r\n%%\r\na = ones(3) - eye(3);\r\nb = zeros(3);\r\np = floor((randi(20)+3)/3)*3;\r\nmat = [];\r\nfor i= 1:p\r\n c = [repmat(b,1,i-1) a repmat(b,1,p-i)];\r\n mat = [mat;c];\r\nend\r\n\r\nassert(isequal(cComponents(mat),p))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":40,"test_suite_updated_at":"2014-12-07T06:39:46.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-12-06T07:19:29.000Z","updated_at":"2026-03-30T17:12:13.000Z","published_at":"2014-12-06T07:19:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an adjacency matrix of a simple undirected graph, find the number of connected components.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2685,"title":"FloydWarshall","description":"Our task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\r\nExample :\r\n input= [0 1 Inf Inf \r\n Inf 0 2 Inf\r\n Inf Inf 0 3\r\n 4 7 Inf 0]\r\n\r\n output= [0 1 3 6\r\n 9 0 2 5\r\n 7 8 0 3\r\n 4 5 7 0]","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 307.9px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 153.95px; transform-origin: 407px 153.95px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.5px 8px; transform-origin: 372.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOur task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.5px 8px; transform-origin: 29.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e :\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 183.9px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 91.95px; transform-origin: 404px 91.95px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e input= [0 1 Inf Inf \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; 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block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4 5 7 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = floydwarshall(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 1 Inf Inf;\r\n Inf 0 2 Inf;\r\n Inf Inf 0 3\r\n 4 7 Inf 0];\r\ny_correct = [0 1 3 6;\r\n 9 0 2 5;\r\n 7 8 0 3;\r\n 4 5 7 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 8 Inf 1;\r\n Inf 0 1 Inf;\r\n 4 Inf 0 Inf;\r\n Inf 2 9 0];\r\ny_correct = [0 3 4 1\r\n 5 0 1 6\r\n 4 7 0 5\r\n 7 2 3 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 3 6 Inf Inf Inf Inf\r\n 3 0 2 1 Inf Inf Inf\r\n 6 2 0 1 4 2 Inf\r\n Inf 1 1 0 2 Inf 4\r\n Inf Inf 4 2 0 2 1\r\n Inf Inf 2 Inf 2 0 1\r\n Inf Inf Inf 4 1 1 0];\r\ny_correct = [0 3 5 4 6 7 7\r\n 3 0 2 1 3 4 4\r\n 5 2 0 1 3 2 3\r\n 4 1 1 0 2 3 3\r\n 6 3 3 2 0 2 1\r\n 7 4 2 3 2 0 1\r\n 7 4 3 3 1 1 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n\r\n%%\r\nx = [0 3 Inf 5;\r\n 2 0 Inf 4;\r\n Inf 1 0 Inf;\r\n Inf Inf 2 0];\r\ny_correct = [0 3 7 5\r\n 2 0 6 4\r\n 3 1 0 5\r\n 5 3 2 0];\r\nassert(isequal(floydwarshall(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":32478,"edited_by":223089,"edited_at":"2023-01-03T06:19:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2023-01-03T06:19:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-11-23T22:43:29.000Z","updated_at":"2026-03-30T15:58:21.000Z","published_at":"2014-11-23T22:44:41.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOur task is to find shortest paths between every pair of nodes. Floyd-Warshall is a graph algorithm for finding shortest paths in weighted graph. The input of a function will be in weighted adjacency matrix representation. If two vertices does not have any edge than this matrix has Inf value. Function will return a matrix with values of shortest paths between each pair of nodes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ input= [0 1 Inf Inf \\n Inf 0 2 Inf\\n Inf Inf 0 3\\n 4 7 Inf 0]\\n\\n output= [0 1 3 6\\n 9 0 2 5\\n 7 8 0 3\\n 4 5 7 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61063,"title":"The Bridges of Nedsburg","description":"The fabled city of Nedsburg consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\r\nTherefore, instead of a map, visitors to Nedsburg are given a connectivity matrix (C): a square matrix of 0s and 1s, where C(j,k) = 1 if there is a bridge from island j to island k, and C(j,k) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means C is not necessarily symmetric. That is, it is possible to have C(j,k) = 1 (you can travel directly from island j to island k) and C(k,j) = 0 (you can’t travel directly from island k to island j).\r\nBecause Lord Ned likes to torture visitors with math problems, the Nedsburg transit system has an n-bridge pass which allows you to cross up to n bridges in one journey. Not surprisingly, the pricing scheme gets eye-wateringly expensive as n increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of n that allows them to get from any island to any other island in one journey.\r\nTo prepare for your upcoming trip to Nedsburg to watch the Nedball World Cup, you need to write a function that, given C, returns the minimum n needed so that there is a path from every island to every other island in n steps or fewer. That is, find the smallest n such that every pair of islands is at most n bridges away from each other.\r\nA handy bit of math that might help is that the value of represents the number of paths of exactly m steps from island j to island k. (Here, is the matrix power; that is, , using matrix multiplication.) Therefore, if , then there is no way to get from j to k in exactly m steps. If , there is at least one path (of m steps) from j to k. Therefore, one way to solve the problem is to find the smallest n such that, for every j,k pair, there is some m ≤ n such that .\r\n\r\nExample\r\nConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\r\n\r\n\r\nThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\r\nC = [0 1 1;0 0 1;1 0 0];\r\nC^2\r\nans =\r\n1 0 1\r\n1 0 0\r\n0 1 1\r\nThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, n = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\r\n\r\nAssumptions\r\nWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite n exists for the given connectivity matrix C. This also means the problem is trivial for one or two islands, so your solution only needs to work when C is at least 3-by-3.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1998.43px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 999.213px; transform-origin: 408px 999.213px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe fabled city of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 42px; text-align: left; transform-origin: 385px 42px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTherefore, instead of a map, visitors to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e are given a connectivity matrix (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e): a square matrix of 0s and 1s, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 1 if there is a bridge from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is not necessarily symmetric. That is, it is possible to have \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 1 (you can travel directly from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) = 0 (you can’t travel directly from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 52.5px; text-align: left; transform-origin: 385px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBecause Lord Ned likes to torture visitors with math problems, the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e transit system has an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-bridge pass which allows you to cross up to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bridges in one journey. Not surprisingly, the pricing scheme gets eye-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewateringly\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e expensive as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that allows them to get from any island to any other island in one journey.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo prepare for your upcoming trip to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedsburg\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to watch the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, you need to write a function that, given \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, returns the minimum \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e needed so that there is a path from every island to every other island in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps or fewer. That is, find the smallest \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that every pair of islands is at most \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bridges away from each other.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 128.2px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 64.1px; text-align: left; transform-origin: 385px 64.1px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA handy bit of math that might help is that the value of \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"53\" height=\"21\" style=\"vertical-align: baseline;width: 53px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e represents the number of paths of exactly \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps from island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to island \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. (Here, \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"22\" height=\"21\" style=\"vertical-align: baseline;width: 22px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the matrix power; that is, \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"91\" height=\"18\" style=\"vertical-align: baseline;width: 91px;height: 18px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, using matrix multiplication.) Therefore, if \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJ4AAAAqCAYAAABC3uIYAAAAAXNSR0IArs4c6QAADIJJREFUeF7tW32wVGUZf553717uhzlKxRVY7vnYFfIyOWqlpZZMaBooJBMmJuZEaBqGMiao6ATiB4qOaTom+ZFpqKiJmTaaiplmk5RNCensnnOWeyHRqUziwu6efZ/2Yc5hXs/d3Xv2667X2fMPw97383l/7/Pxe54XofW1JNAECWAT5mxN2ZIAtIDXAkFTJNACXlPE3pq0BbwWBpoigRbwmiL21qQt4LUw0BQJtIDXFLG3Jm0Br4WBpkigBbymiL01aQt4LQw0RQIjBrwJEyZ0jRkzZg4AnAAARwLAmsHBwQe7u7svIKJLiGh7W1vbbAAYcF13HiKuLLTZj4hmOI7zalOk05q0YRIYMeDxDhh87e3taxHxeCHEPCnlDER8XEo5BxG/Q0RzEXGmlPKFSCQynohuQ8QFlmXd3TAJ1HngadOmtfX394+XUuqRSOSvyWTy/TpP8ZEYrmLgsWAdx/ksALB2+hIiHgIABwLA2wCQIqKXhBC/B4AeIjoqEon8wBd+LBYbG41GnyAiRMSXAeD+TCaT9MD4aQD4IxE9ouv6c+l0+iYA+AYinmxZ1p9KSTsWi3W2t7cvJqKUbdvrw5yKpmnjhRD38voB4Fzbtn8OABSmb7E2U6ZM+Vgmk7kMEb/saXNu9iwRneY4znvBPqZpzpFSHuy67i0DAwO7q533w9Cvt7fXFEIsEkKwongHAKYAAJ//Wtu2d5RaYyXAE5qmnSiEuKUAmASDhM0lALxk2/a7fX19XXv27DmRiG4EAM2b8FZN05Zs3LjR5f9rmna4EOI3DFTWbo7jPBGLxSZEo9EnAWAqAJxn2/Y9sVjsAA+gGdd15w4MDPy72AYmTZo0IRKJ3IqIGyoBj2EY8wHgPm/Ml3O53KxSc1RyuPF4fJyU8tcAwBfzA3sPjIO6rs9CxHmu6y7p7+/fXsk8H4a2ngJagIg3IOKy3t7eO/mc+/r69hscHLweEWcUFNF827ZfKrbeUMAzDKMHAK4HgLMA4D+IeLEQ4oFkMpkJDppIJKbm8/nHAGAyAJxj2/Zav41pmt8morv4NmQymQu3b98+qOv6cYj4LCI+2tHRsXDz5s3/8wGKiPdalnUpAMjgPIZh8M26BxHXWpbF2iu0xjJNc7KU8jFE5DWu0DRttX85ajlUb9ynEDEOAHNt236kzHhomubZUsoziejMdDr9z1rmHum+hmEwFn6KiHzRzrYs67/+GuLx+CQp5QYA6I5EInOSyeQbwfUNCzxN0wwhBJuiYwAgXQ7F3uBoGMYiALiFfTnLsp7j3xOJxBjXdW9HxLkAcLJt27/j3w3DuKzw7w8R8VTLsngT4AOUiL7mOA5v4AOfZyrXEdEruq5fWSVoRE9PT+eOHTt21evQTNOcTkS/ZTlJKb+aTqe3lBubtcbWrVuvICK9s7Pze3zp6rWWRo6TSCTirutuQMSpRDTPcZwHgxrdO9dVRPSLbDa7kJWM2qYs8PwDBoDjPE031wdSuY15GusuRDzdsqy3uK0HYAYW+4Hz2fdh3yibzTKoD/JA9nZfX1/77t27f8xzsn8npZzguu7ffFPIAJZS/oiIjhRCzE6lUv2NFHIlY3vCvhoANrS3t89/8803dw7X39cORPS4ruvXVHmJhpumrn83TfN8DvzKXTDfkgHAHiHESalU6pVQwOMItKOj4zYiOps7ML3R29t7VRjBTJ48+ROu6y7PZrMrfcCYpjmTiJ5ExCWWZd3MptEwjEMB4GlEvN83qYqflCYiNsG7bdu+3ze33ji/RMQLLcu6va4SrWEw5RLNJqJrHce5PKz59w7ycinlKel0+s81LKPhXdV9lgugFEVzSDF5lNR4hmGwSXwAAKKFQOAN5tiSyWSqyp2xM301UyNqlFrMpBqGoRHRo4g4znNaH/bB7jmuP0PEY6WUJ6XT6b9Uuh6+UF1dXeNzudyhkUjkoF27dt1XD3Or+nfeHve6DWE+xadd19vbe3GYyx1m3Ea0UQHFARoRnes4zp7gXKx8crkcB41HFbMARYHn0R7rAOAr3oDLbdu+JuwNbsSGecx4PH60lJKj4ldLURWl5o7H45+RUp4KALMAgKkb/spFnhVtw9foBQpoi5RyZjqdtg3DOAERzyCiPgDIAwD7u+vHjh27ZdOmTTl/Al3XD0DEhzkgC+MbVrSwOjc2TfNYInqeFRIirrYsa1mxKRTO9gxVJn7bosBT7HMUAHYWs9F13k+o4RQfam1nZ+eizZs3Z0N1VBp5e9vo/XSWR8NUOkywva/ROQJ/aHBwcIGvRb3LchcRfddxHA6ohkTfil+7sFLCXGEKatoDEU1zHOfF4QZRAih2v8ICb0iwVRR4hmFcBQDLvUW8JoSYmUqlmBxs2tfT09Pd1dXFVAyTyiU3PNwCDcP4OmsdAHinWnMdnEPRWJwOvNyzDn5AtUxKeR1rwHJrM03zOiJaylRTJZeqCcDzKbFKgAdBYA8BnnrALKhS4fBwB1zvv6s+Q6VaQVmLqpnqRhwHiPETWHMwn+m67jdzudyN27Zt+9dw8jAMY2Ehq3hnIZddt3UNN2c1f1cubiXA20lELBdOOuz9hgAv4BRymzW2bV/SbP8u4LxXlb8NRGT19O98LfC6EGKWlPJTBVL8mGw2uzpsSkzRXPt8xGqA0eg+qqtSV1MbBF4tZq2eQtA07RAhxNNMCVar8QKZhbr4d0wCe3llrrLhbAiz9MuI6KwixGpJkSjAC0U+11O2lYylaHdmHUL5eJxHF0LM8DndohrPT+R7mYqy6rSSBdfatk7A28sl1tO/SyQSn8zn85xd+YK6RwZhV1fXt8JmI0YR8DiTxVQRF4eUpFMCOBpSMDHE1KoRlifID0RptQKo2v5qAr5ajadExXXzo3RdP4pzzQDQwUUOhZx2F6cLK2UDFOC9nsvlTh4YGNgWRlYjHVwEaJKSFTgBvu8mTdOWqvxkKTrldERkHo+/tyKRyIww5PHEiRM/3t7evjSfz9+xdetWK4zgwrYJ+GerbNu+Imxfbtco/07X9cWIyJmYvYDp6OjoyOfzTzEnR0R3t7W1nV+smCK4dmWckodZbL8jDTxeg5IyK+mPKnwfB6izHMdh/nXfVxR4rF3y+fw6r76MG3/ftm3On5asAOESpWg0yvm7JyqtFgkJIDUirTjgUW/gMBpTcFlWd3d3brhca+D276VBxo0bJ9Pp9GoAWFKoztnup8G8CuzpkUjkmWJAVOiUugU9IeVacTOl+uTwUlkaxbo8k8vl5gXLzkqmzDyikHOkB3GBgJRyQTqdZl8mWKIkTNM8iYjWENFKx3EealQErGQHQifhfakqxGdJ/k6pFrmywMfliGhVucR9LBab6NUSHqaCWdO0I4QQv+Kia0T8iRBisZTyeCnlVMdxbgjKR9f1Dm7HZWclqj0qBkejO/hlUUS0Plh9ogBTJ6JTixHTZatTvDQTJ+L5jQR/zyAil8D0SykjQogjWFAA8J4Q4qJUKrWpkRtmrdrW1ra3zstPS4WdT7mBf4hEIpx3fjfYtwiVVDbCVDI8fDH35Y6DAAYAzpQMCiHOKUbEK/7r/mHdmrD7blQ7b48cya8AgIu4gJeVEleER6NR/o2Lbbkek4O5IZZy2Ho8rwxpOms8RDza04C8H5uIXuDIRtf1l0cosc3Fk0u52iFEoeU+mQdMYklT5lEjnD3grA0HC0MYd/UgFTAP8cu8goaViMi1ic9LKc8rlb1QALxa07QVIyTLemAS4/H4VCnlBazdvXrNg7k63XXdO8pVVg8LvHqsrp5jKPVrW4oVGBabSzWJYQHraaH7pJQXDVfQWcv+PM2xhk1xjRVAtSxjxPuOOuCxhLySrXsR8TS/armc5BSNkgxronVdP5ErqDVNu7SRGkjX9c/zmxEAWBT2sdKIo6QBE45K4Ck+1HTXdU8LqnT++86dO9ErPeJSfC6vXwUAQ/ikYjL13o1cKaW8sJFvIbjCm4tgAeDF0VJ9XC8Mjkrg8ebZ98zn88sLB5fIZDKL/ES8R5vwY6MDEfGUgjne1tbWth4RY0Q023Gcf5QTnmman+PHNwBwTbnnebUeAPuAXon/3zVNu7mRWrXWtTai/6gFHgtDeWL3xWw2u5jBp2QS+L0DPxjnyJtLjhY3kuqp5HA8on01Ij5vWRazBENe0VUy3mhsO6qB5wvce1S8v+M4r3tgXMRvO7hSmVNYhdfjKyzLeq1R/GKlB6/r+mFSyvfrnd2pdB3NbP+RAF4zBdiauzoJtIBXndxavWqUQAt4NQqw1b06CfwfBCeAlDD//O8AAAAASUVORK5CYII=\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, then there is no way to get from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in exactly \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps. If \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, there is at least one path (of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps) from \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Therefore, one way to solve the problem is to find the smallest \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that, for every \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pair, there is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003esome\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ≤ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e such that \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"79\" height=\"21\" style=\"vertical-align: baseline;width: 79px;height: 21px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 366.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 183.4px; text-align: left; transform-origin: 385px 183.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"704\" height=\"361\" style=\"vertical-align: baseline;width: 704px;height: 361px\" 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6elBT08PwuEw4vE4jEYj/H4/mpqa0NbWhpqammKXSUREpBvhhIJtAxMIJ7K4aI4TTT4LRKFMJ3qcgdMiI5VVoWpaQf8NLpvrRiqrYlt/BJc3uyCV60QbKntlE0CSySR++9vf4ve//z22bNmCsbGx036vz+fDJZdcgtWrV+P222+Hy+UqYKVERET6kc6q2Hs0jsFgEg6zjI92+OG2lM3twzmxGiWMxzNQchqMcuFCgCAAFzY68Pt3ArAYRVw021mwn000ncqi/+53v/sdLr74Ytx7771Ys2bNh4YPAAgGg1i7di3uv/9+LFmyBE899VRhCiUiItKRo5EM/r93juLgaAKd9XZcvcBb8eEDAOxGGSpQsInox/NYDbhyvge7hqMYiaQLXwDRNND1u4imafjiF7+Ixx577JRfr6qqgs/ng9PpRCQSQSgUQiAQyPuevr4+3H333Vi/fj2efPJJyLKu/0mIiIjOWyKTw96ROHoCCdS5jLi4yQW7iZ+P7zMZBESSOSjFSCAA5ldbEU5ksbVvAjd2VsEgcSgW6Ytu300URcEdd9yBZ599Nu+4z+fDZz7zGfzVX/0V2tra4PV6IYoiVFVFOBzGzp078ac//Qm//OUvEQ6HJ8976qmnJodxCRU6ppWIiOjweAqbD4bhMMtY1uLmikun4LUZEE8ryCgqYCrO6l9LZjtxpGsUb/SM4+oF3qLUQHSudDsE6x//8R9PCh+f+MQnsH37djz66KNYvnw5/H4/RPHYSxRFEV6vFytXrsQPfvADbN++HZ/85Cfzzn/66afx3e9+t2CvgYiIqFSE4lm8PTCBbQMTqHGasHKBl+HjNKT3HlRmi9QDAgCyKODyFjcC0TQOBBJFq4PoXOgygLz44ov40Y9+lHfs/vvvxzPPPIPZs2dP6Rpz587F008/jc9+9rN5xx9++GH09fVNW61ERESlrmsoihd2jSKcVHBtmw9XzffAzGVeT0sUALfVcKwHpIiq7EYsaXRi28AExmLZotZCdDZ09+6iKAq+/vWv5x1buXIlnnjiiXO63k9/+lNccMEFk+10Oo0nn3zyvGokIiLSg9FoBq/uC2LvkTgumOXANQt8cJp1Ozq7YCRRgN0kIVgCN/2tNTbUOk3Y1j9RlEnxROdCdwHkhRdewDvvvDPZNhgM+M///M9znrchCAIefPDBvGNr1qxBLpc7rzqJiIhK2bb+CazpGoUsiVi10I9FDQ5wCuTUiIIAq0FCIqNAK4Gb/qvmuZHK5vDWwESxSyGaEt0FkF/84hd57Y9//ONYuHDheV3z6quvzmt3dXWht7f3vK5JRERUikYiaby0N4iDY0lcOteFq+Z74OLSumdFEACzUUI8k4NaAglElkRc1uzGYCjJoVikC7p6xwmFQtiwYUPesbvuuuu8r1tdXY0nnngCwWAQwLHlfa1W6zld64033sDQ0NDk5PdzpaoqGhoasHz58vO6DhEREQAksyre6p/AaDyDZp8VlzS5GDzOg9MsYfewAlUDirMOVr46lwnNfite2juGjy2qgoND6aiE6eq3c/v27XlL5/p8vmm5QRdFEX/3d3933tcBgG9961t49dVXp+VaK1euxGuvvTYt1yIiosqkqBoOj6fQE0ggrahY3uJBtcNY7LJ0z26SoGkaNE0DUBpj1xbW2zE8kcY7h6K4ar6n2OUQnZauhmDt3r07r71o0SJ4PKX1B3a+PR8zdS0iIqo8wXgW6/aMYdvABBrcJtzY6Wf4mCYGSYDDLCGWLp05oyZZxLIWN4Yn0tgzHC92OUSnpasekAMHDuS1W1tbi1TJ6TU0NGDWrFnTdi0iIqKzpagaegIJ7DkSg8kgYlWHn6tbTTOjLMIkSxiLZeG1GYpdziSP1YDFDXbsOBxBvccEN4fZUQnS1W/l+3M03ldbW1ukSk7vZz/7GRRFOe/d1DVNgyzr6r+HiIhKQCCawZaDYSiqhrZaGzrq7MUuqSzJogiTQUQ8rRS7lJN01NkxFE5j88Ewrm/3QRJLY4gY0ft0dYcbj+d3J5rNpbdDq8lkgslkKnYZRERUYdKKip5AAl1DUXisBqxodnOS+QySRQEmWSypIVjHu7zZjRd3j2L/0ThDKJUcvjMRERHp3IFAAu8ejsJvlXHVfA9qnCY+9Z5hggC4LTL2jZTmXAu7ScIlTW683h1CtcMIv51zf6h06CqA2Gy2vHY6nS5SJaeXy+Wgquq0DMESRRGSVAqL+xERUSmKpXPoGoriQCCB9jobFjc4YJK5gEmh2IwSFFVDWtFgkksv8M32mjHHZ8bmg2HcvKgazKRUKnQVQHw+X157ZGSkSJWc3t///d/j9ddfn5Z9QFasWIEnnnhimiojIqJysuNwFPtH4nBaZNzU6ecT7iKQJAGapmEikUW1szT//a9o9uClvUF0DUVxwSxHscshAqCzANLS0pLX7u7uLlIlp9fd3Y29e/dOy7VKcZI9EREV13gii93DMfSMJrCowYGLZjuLXVLFcpgk+O1GJDKlOQ8EOLZccGe9Ha91B1HlMKLexXmqVHy6CiDt7e157Z07d2J8fHxa9gLp7+9HLBYDcKz3YdasWfB6vWd9nUwmc961zMS1iIhI37I5DTsPR3FoPAWXRcLNnVWo4p4eRWV+b7hbKJFFEyxFrub0ZnvN6Kx34J3BCKo6/DBIHItFxaWrAHLRRRfB4XAgGo0COLYs7xtvvIFbbrnlvK6rqipuvfVW7Nq1a7K9bt06XH/99Wd9rS996Uu49dZbp2UOSHNz83ldg4iIysN4Iou3ByMYDqexdI6TqxqVCFkWYTaICCWyxS7ljDrr7RgMprCtfwJXtLiLXQ5VOF0FkKqqKixfvhxr166dPPbf//3f5x1AhoeH0d3dDVVVAQCSJGHevHnndK3bbrvtvGohIiJ6XzKrYlv/BALRNOZX27B0josby5UQAYDTIiOcUKBpx1bGKlUmWcRFcxx4szeMJp8F9W4OxaLi0d1SGffdd19e+7nnnsP+/fvP65ovvfQSUqnUZHvRokWYO3fueV2TiIjoXGkAhsIprNs9ikA0jUvnunHBLAfDRwlymiWIAhBOlt6GhCea7bVgfo0N7xyKIJ1Vi10OVTDdBZCbb74ZnZ2dk+1UKoUvfOEL53w9RVHwwx/+MO/Ybbfddt5DqIiIiM7FRFLBa/tDeLM3jEavGbdeUINGT+ltvEvHGCUJKjQkS3gi+vE66x2Ip3N493C02KVQBdNdADGZTPiP//iPvGMvv/wyPve5z53T9b7yla+gq6trsm2323HPPfecV41ERERnS9OAg6MJvLwviEhSwTVtPlw028UJwyXOaZGhaYIu5oEAgEkWcOV8DwZDSd3UTOVHdwEEAG688caTej2eeOIJ3H777RgcHJzSNYLBIO655x489thjeccffvhhzJo1a9pqJSIiOpOjkQxe3hdE72gC86utuHlRFXw2Q7HLoimwGI7dSh2NlN7myKdT5zJhrt+CN3vDyOa0YpdDFUi3g0kfffRRjIyM4He/+93ksWeeeQavvfYa7rzzTtxyyy3o6OiA0+mELMvIZrOIx+Po7u7GunXr8Ktf/QoDAwN51/zbv/1bfPGLXyz0SyEiogqVUzXsHYmhayiOWR4TPtLohM0kFbssOktui4yMoq85FQvrHOgPBvDuoQgubnIVuxyqMLoNIAaDAU8//TS+8IUv5O0WPjo6ikcffRSPPvoo3G43/H4/TCYTUqkUxsfHEQqFTnm9e++9Fz/72c8KVT4REVW4g2MJ7BqKQZYEXNHsxhwf53nolccmIxDNIpVVYTboY3CJxSji8mY3Nh4IY36NjQscUEHp46/kNERRxOOPP45nnnkGHR0dJ309HA6jp6cHu3fvRm9v7ynDR2NjI37605/i5z//OSSJT52IiGhmJTM5bO2fwEt7Q6hxmnD1Ah/Dh85V201IpHO6m1PR4DajzmXC+u5TP5wlmim6DiDv+8QnPoFt27bhF7/4BW666Sa43R++wY7NZsPy5cvx4x//GDt37sT9999foEqJiKiSHQgk8Or+EEYiadzU6celc12TcwhIv1wWGRNJBcGYvgIIACyeZUc8ncNOropFBVQ2/W1WqxX33HMP7rnnHoyMjKC7uxvd3d2IRqOIxWIwGo3weDyYO3cu2tra0NjYWOySiYioQsTSObxzKIKDo0ksaXRgYb0dksjVrcqFLAlwWCSM66wHBAA8VgOWzHbi7YEI5vgscHEoFhVAWf6W1dbWora2FldddVWxSyEiogqm5DTsHIriaCQDm0nCtW1ezOKeHmXJazUglVWh5DTIOls6ua3GhoFgEtsPRbByvrekd3Sn8sB+XyIiohkQimfx/K5R7D8ax/xqK66a72H4KGO1ThMEACmdrYYFAIIAXDrXhaHxFPaNxIpdDlUABhAiIqJplHhvkvnL+4Lw2Qy4qbMK86qtxS6LZpjHasDhcBqRpFLsUs6Jx2pAW60de47EkcrqL0SRvpTlECwiIqJiGI1msOHAOFKKihXs8agoLqsMUQAmUgrqYSp2OedkSaMDY7EMdg5FcQn3BqEZxB4QIiKi8zSeULClL4wNB0KocRpx24XVDB8VRgDgtsoIRDLQdLq5uCQKWNTgwN4jMd0tKUz6wgBCRER0Hg6OJfHy3jGEE1ksn+/B8nkeWI3cV6oS1ThNSCk5ZHP6HcLU4DahtcaGNw6MI6fXJEUljwGEiIjoHIxE0nhtfwjdR+Nor7Nj1cIq1Dj0OfSGpsccrwWprIq0ou8b98WzHEhmVfQEEsUuhcoUAwgREdFZ0ADsPBzFut1jkETgsmY3Ouvt4Mql5DRLSGZySGVzxS7lvNiMEpY0OrD/aBxJTkinGcAAQkRENEXDE2n8efcYekcTuKzZjavme+Hmxm30HlEUUO00Yjyhz5WwjjfXbwUgYMvBcLFLoTLEAEJERHQGaUXFzqEY1u0Zg8Ms47p2HxbU2IpdFpUYWRTgsxkxPJGCqu9RWDBIAi6c5UAgmsZwOF3scqjMMIAQERGdhqpq6B1N4M3eMPqDCSxv8eCKFjccZvZ60KnZTTL6g0koek8gAGZ7zWjyWdA1FCuL10OlgwGEiIjoFCZSCv68N4i/9E2gxmnExxZVYT43FKQz8NllWAwSojrdkPBEnfUOpJQcBoLJYpdCZYSPcIiIiI6j5DTsD8SxfyQOi1HCR9t9qHIYi10W6YTNKKHRY8ZIJA2f3VDscs6bzSRhfrUV2wejqHebYTHw2TWdP/4WERERvSeSVPDcjgB2Ho5iYZ0dNy70M3zQWTFIInIq0DdWPj0GrTU2yBKwayha7FKoTDCAEBFRxUsrKnYNx/DnvWOwmySs6vBjQS0nmdO58dkNCCeVstnITxYFXDnPgz1HYhiZ4IR0On8cgkVERBXt8HgKmw9OwCQLWDrHhdleC0Ru6kHnoc5lRJXDgLFoFjXO8uhB89uNaK2xYddwDLUubrhJ54c9IEREVJEiSQXvHo5i+2AEbquMlQu8aPIxfND5sxllyKKIoYlUsUuZVp31dqQUFYfHy+t1UeGxB4SIiCrOgUAC2wYm4LbIWDbPA59N/5OFqXQYJAGKquFwKIWPNDqLXc60cZhlzPKY8dbABGqdJsgS0zqdG/aAEBFRxRhPZLHxwDjeORRBa7UN17f7GT5o2gkCMNdnhiAIyCjlMQ/kfa3VVqga8M6hSLFLIR1jACEiooqw50gcL+waQzan4epWL5bOcfIJLs2YKocJVqOIo9HymrRtNUpoq7Xh4GgCkVR57HVChccAQkREZW0slsGGAyF0DUXRWW/HNW1eLq1LM85plhBPqzhShqtGLaixwWGRsX2AvSB0bjgHhIiIylJaUbHjUBQDoSQW1Nhw4SwnnBZ+7FFhSKIAn11GIqMWu5RpJ4kCljS6sKl3HEejGdQw0NNZYg8IERGVFU0DDo2n8Pr+EIYn0riixYPFsxwMH1RwtS4TVE1DPJ0rdinTrs5lhNdqwN7hGNTymuZCBcAAQkREZSOeyeGVfUG82TuOaocRH1tchQY39yyg4qhzmpBWVIzGMsUuZUZcMMuB4Yn9f+s8AAAgAElEQVQ0jkbKb5gZzayyeBwUCoXw5ptvYseOHRgeHoaqqvB6vWhtbcWVV16J5ubmYpd4Wps2bcLIyAgEQYDVasX1118PUSzNXPj222+jv78fgiDAaDTiox/9KEwmfrATUfFpGtA7lsCeIzFoGrCy1Vc2G8CRflmNEjQVGI1m0eSzFLucaeezGzCv2oq3ByO4qbOKe+jQlOk6gGQyGXznO9/Bz3/+cwwPD5/ye2w2G/7mb/4G//Iv/4KmpqbCFngGa9aswS233DLZ9vl8GB4ehtFYeh+aW7duxYoVK5BKHdt8SJIkDA8Po7q6usiVEVGlCycV/KUvjHRWxWyfBZ11dq5uRSXDbzdgNJZGOqvCZCjNB4zno73Whv1H4xgIJjHXX34hi2aGbv8ShoaGsGrVKjz00EOnDR8AEI/H8atf/QqXXnopXn/99cIVeAZ/+MMf8MlPfjLvmNNZmpsVrV+/Hrfccstk+AAAl8sFQeAHPBEVTzan4UAggZf3jiGraLhyvgcXznIwfFBJOTYPBEhky28eCHBsc8IFNTa8NTBR7FJIR3QZQJLJJG677baTAsW8efNwyy23YPXq1Vi8eHHe1wKBAFavXo29e/cWsNKTqaqK7373u7jtttuQTCaLWstUPP7447j++usRCASKXQoR0aRD4yms2RlAz2gclza5cf1CHzxWbihIpWeWx4SsomIsli12KTOmo84OURTQNRQtdimkE7oMIF/96lexbdu2ybbZbMaTTz6Jrq4u/PGPf8Tvf/97vPvuu3jhhRfQ0NAw+X0TExO47777oKrFWRJv3bp1uPLKK/GNb3yjKD//bGzYsAGrVq3C5z//eWQy5Tl5joj0J5nJYfuhCNZ3h+C3G7FivheNXjOMki4/zqgCiIKAWpcJ44ksVK08l4uymyS0VlvRNRRDNld+yw7T9NPdHJADBw7gJz/5yWRbkiT89re/xerVq/O+TxAE3HjjjVizZg2uvfZahEIhAMCbb76JF154AR/72McKUu+bb76JdevWYe3atXmhCQDcbjcURUEsFitILWeyfft2rF27Fi+++CLeeOONvK85HA5IkoRwOFyk6oio0h0IJPDuoQjMBglXL/CiwW0udklEU9Lks2D7YASprAqrUSp2OTOipcqKvmAS2wejuHSuq9jlUInT3SOjJ554ArncB+Mo77zzzpPCx/EuvPBCPPjgg3nHfvSjH81YfSf60pe+hIceeuik8PGRj3wEzz77LGw2W8FqOZOHHnoI//zP/3xS+GhpacEf/vAH1NfXF6kyIqpU2ZyGQ+MpPLcjgA3dIdS7Tbip08/wQbrisxswkVQQSZXnPBDg2IpfC6pt6A8mES3DfU9oeukqgKTTafzv//7vZFuW5ZPCxancf//98Pv9k+3XX38dvb29M1LjiaxWa17b6XTiwQcfxIYNG7B06dKS6f0Ajg1lO57FYsHnP/95bNmyBddccw2iUY7tJKKZk8oeGyd/YDSBtwcm8MedATz++iH84KUB9I0lcW27D8taPJC41ifpjCgIcFlkDIZKf+7n+WipssJrk7HvSLzYpVCJ09UQrJ07d2JgYGCyfemll6Ktre2M57lcLtxwww34zW9+AwDI5XLYsGEDWlpaZqxWANA0DclkEoIg4KKLLsLq1avx6U9/GnPmzAEADAwMlNRKUu+vcrVo0SJ8/OMfx5133okFCxYAACKRSEnVSkT6ls6qSOdUHI1kEIimMRbLIhTPHlstKJNDKJ5F31gS4wkF17X7cO+y+rIdukLlTxYFVDmMOBBI4COznZDLNETLkoAFNXa8PRBBS5UFXhsXhqBT01UAOXFo0MqVK6d87rXXXjsZQIBjS8vefffd01XaKWmahu9///vw+Xxob28/6euldkP/9a9/Hd/+9rfR2dkJScr/oC+1WolI3xLZHAZCKfQGEhgIJZFRVCiq9l4viIJYWoHdJOG6di/+z8U1DB+ke81+C/rGkkhmcnCYdXX7dVZme83YcySGA4EE54LQaenqL2DPnj157VPd1J9Oa2trXrunp2daavowoihi+fLlM/5zpssll1xS7BKIqEI4LTLaamyYV2VBKqthPJHBX/omsLEnjFgqh1keE/wOI65Z4EWVvfQ2ZyU6Ww6zfGxTwmi2rAMIACxqcGDzwTBaa6xcHptOSVdzQPr6+vLac+fOnfK5jY2NsFg+2KHzyJEjutiHg4ioHEmCALNBhN0kw2OVEYwrcJhlNPksmFdtQZXDiItnO9FRZy92qUTTwiiJ8NuNODiWKHYpM67BbYLdJKEnUP6vlc6NbgKIqqo4dOjQZFsURfh8vimf73K58nYaDwQCmJjgrp1ERMU0GEph7a4xDIdTWDrHhRa/BaoGtPgtuKzZDY7+pHIhCEC1w4jxRBaxMl4N630XznKgbyyJiaRS7FKoBOkmgGSz2bweC7vdDrt96k/GzGYzXK4PxiKm02kkEkzmRETFEIhm8PLeIPaOxNDgNuHmRVVwmCSMxTPw2gy4vNkNs0E3H1FEU+KyyKhyGHEkki52KTOuxmmCx2bAvhGuiEUn0827eyqVQjabnWybTCYYjVMfFyzLct4QrFLaAJCIqFKomobdR2J4cfcoNACXNrmwZLYTJllEXzCBtKLi6lYval2mYpdKNO2sRgkOs4yBYPkPARcEYFGDHUcjaURT7AWhfLqZBaUoSt4GhLIsn7RS04cRRRGy/MHL1TQN6XTxn0BwdSkiqhSHx1PYORRFKqvi8mYPWqqsOH41UkUFls5xYVED531Q+fJYZRwKpRBL52A3lffqblV2I1wWA7qGYriixV3scqiE6KYHxGAw5AWIbDYLRZl6olZVNa8HRRTFvB4RIiKaGWlFxa6hGF7cPQaLQcKqhX7Mr84PH6qmwWmWsLDOzo0GqazVOc2wmyQMhVPFLmXGSaKAuX4LhsMpzgWhPLrpATGZTHkBJJVKIZPJTPn8E+eQyLIMm802rTWeLUEQ2ANCRGVtIJjEvpE4klkVKxd4Ma/KesrvEwXhvR4RvidSebMYRViMIobCaSyoKe59SCHUu03w2ozYNRzDMvaC0Ht01QNyfGCIx+OIRqNTPj+VSmF8fHyybbFYih5A9IZhiYimKpVVsbV/Aq8fCMFtNeDmRVWnDR/vY/igSlHnMiOtqBXRKyCLApqrLAhE0xhPZM98AlUE3QQQURQxe/bsybamaRgdHZ3y+aFQKC+w1NTUwO0ubhLX0w29nmolouLaNRzDq/uCSGZyuHKeB5fOdcEg8T2E6H2zvWZomoajFbAaFgDM9lpgN8lcEYsm6SaAAEBzc3Neu7e3d8rnDgwM5E06r6urO6tVtGaKnm7s9VQrERVeNJ3Dn/eO4Z1DEcz2WXDVfC+a/R/e60FUiWRRgMUo4Wg0A1UrdjUzTxSA1hobRiJphOLsBSGdBZBFixbltbu6uqZ87p49e/LabW1t01LT+eANPRGVg7SiYsfhKNbtHoUsCLipswqd9XZuIkj0IeZXWRFL5xCpkCVq53jNsBgkHBwr/yWI6cx0FUCWL1+ed9P+yiuvQNOm9uhg3bp1ee2rr756Wms7V3oJIXqpk4gKKxTP4pV9QewZjuGiOS6sXOCFz2YodllEJW+Wx4x4OodgrHJ6BDrr7RiZSGMiWTmvmU5NVwGko6MD8+bNm2x3dXVh69atZzxveHgYr7322mTbbDZj2bJlM1Lj2dDbTb3e6iWimRNL5/D2YASbesdhkkXcvLgKc30WTiQnOgs1DiMC0XRFDMMCjoWulKKiJ8BekEqnqwAiyzLuuOOOybamaXj44YfPeN4Pf/jDvAnoN954I2bNmjUjNZ4tvdzU66VOIpp5RybSeGlPEEcjaVzS5MK1bT44zbpZ1Z2oZLTV2ipup/CLZjvRH0oimVWLXQoVka4CCAB89rOfhdlsnmw///zz+OEPf3ja71+zZg0ee+yxvGP/8A//MGP1lTOGEKLKFoxnseFACO8eiqDJb8ZH2/2ocZqKXRaRbnltBqQVDYHo1Pc107tGrxk5VcPB0USxS6Ei0l0Aqa+vxze/+c28Y1/+8pfx1a9+Ff39/ZPHxsbG8OMf/xi333573oaFn/rUp7BixYpClfuheENPRHrRE0jg+a5RpBUNF81xYUmjk0vrEp0nURDQ7LciEE2jQkZhQRYFfKTRia6hKDI59oJUKl32mX/961/H5s2bsWbNmslj//7v/46f/OQnaGlpgSRJOHz4MAKBQN55bW1teOKJJwpd7ofSSwjhru1ElWk0lsGuoRiC8QyWNDrQUWeHJPK9gGg6CAIw12/GS3uDuGBWDnaTVOySCqLBY8au4Rj6x5JorYDd4OlkuusBed9vf/tb3H333XnHYrEYduzYge3bt58UPq666iqsW7cOLperkGWeUTb7wUoQilLaY0CPr+/4uomo/GRz2ntL647BZBBxXbsfixocDB9E08xhluG2GHBkojI2JQQAi0FEc5UFe45wY8JKpdsAYrfb8ctf/hJPPfUUli5dClE89UtpbW3Fww8/jJdffjlvJ/VSIIoinE4nLBYLLBYL7HZ7sUs6LUEQ4HA4Jmt1OBzsESEqU72jCWzqGcdAMIkLG524otkNt0WXHeZEJc8oi6hzmdBXYftj1LtMUFQNg6FUsUuhItD9J8pdd92Fu+66C7t27cLu3bsRCASQy+Xg9XrR3t6OxYsXw2QqzUmSVVVV2Lp1K3K5HIBjq3wZDKW5fr7JZMJLL72EbDYLQRAgiiK8Xm+xyyKiaZTMqvhLXxg9gQQWz3LgylYPJD5oIJpRAoB6twm9owkE49mK2UfHZzOiymFEz2gCs73mM59AZUX3AeR9nZ2d6OzsLHYZZ0UURdTX1xe7jCkRBAG1tbXFLoOIZoCmaegZTWLvkRgEAbixswoN7tJ8cENUjjxWGSaDiF1DUaxorYyHe4JwbBnibf0TCEQzqHYYi10SFVDZBBAiIjp7qayKV/eHMBY7Nsm8vc4OmfM8iArKIIlo9JixbySOtKLCJOt2hPxZqXYYYTVIOBCIM4BUmMr4DSciojzZnIruo3G8uHsMqqbh+nYfFjU4GD6IiqTZb4HTLOPweOXMiRAAzPKY0H00gdFY5eyFQgwgREQVZzSWwYu7x9A1FENHnR2rOvyodXHIFVExOcwyXBYZ/cHKmozeUmXFcDiN594dhVopm6EQAwgRUaWIpRV0DcWwtW8CNqOM69p9aK2xckNBohJR6zIhk9MQSZX2svzTSRIF1LlN+NPOUby6LwiNIaQicA4IEVEFODiWwFv9EdhMEpY0OlDv5qozRKWm3m3CniMxHA6l0FFfukvzT7dqhxGapmHdniDMBhGXN7u551CZYwAhIipj0ZSCPUfiGAgl0eg145ImFz/YiUqUWRbhsxnQH0piXrUVxgqZjG4xiKh1mRCMZ7H+wDhUDVjW4oFUGS+/IvG/loioTPWNJfGnnaMIJ7NY1uLhU0UiHWirtWE8oWAsli12KQVjkETUOA3IKCpi6Rw29YxjY884cpwUUrbYA0JEVGbGE1nsG4ljMJTCvGorljQ6Oc+DSCc8VgN8NgOGwmnUuUyohL1ATbIAu+nYJPxIUoHFIGJ9dwiqpmFlqxd8blJ+2ANCRFQmMoqKHYejeGlPELIoYFWHD5c0uRg+iHRmQY0NQxMpJLO5YpdSEFaTBJMswm2RIYsC5ngtsJskbOodxxs941By7AkpN+wBISIqA0cm0nj3UASRVA4XN7kw12epiCenROWo0WvGO4ciOBrJYK7fMnk8lVVhkISyG0opAMipGlwWGa01NixusKPBY0bXUBT9wSRcFhkddXY+TCkjDCBERDqWVlRs6h0/dqPis+DqBT6YDezcJtIzWRTQ6DVj51AUc3wWiAIQT+cwOJ7CbI8ZNpNU7BKnlSgI0KDhI3OcqHMd25hwyWwnrm3zIZlVMRxOIaOoMEjl9borGT+liIh0ajCUwkt7gzgayeCKZjcua3YzfBCViflVVsTSOYQTxyajD4SSCEQyZTkfQhYFWAwSOuvsaPFbYTaIGAgd2xHeYhDRUmUtu9BV6dgDQkSkM8lMDlv6JhBOZtHoseDaNi8sBn44E5UTp0VGvcuEA4EELp3rQk8gibSSg1CGYytlScRcvwV2swyzQYTPakBPII45Xu5XVK74qIyISCcUVcNgKImX9wURSmRx2Vw3ls5xMnwQlYnBUAqHxlPI5DSIgoC2WhuC8Sy6Awn0BxNI51TkynCrcIdJxqVNbtiMx97LapxGjEYzCEQzRa6MZgp7QIiIdGA0msGWvgmomobFDQ7UuUwcbkVUZrw2AzYeGMearlFc0OBAa40VogC8vDeIcFJB1Xs7hpcbn80A8bi3swa3GUZZRO9oAtUOY/EKoxnDAEJEVMLSioqeQAI7h6Lw2Y24tMkFl4Vv3UTlyG6ScEWLG8F4Fmt3j+HtQSMiKQUDwRQaPaZilzdjxBOepciSgHnVVhyZSCOZVWHhw5ayw08xIqISNRhKYWt/GLIo4tK5LjT7rcUuiYhmmMsi42OLqgAAB0bjUFUgm1MRSeWQyOSQUTSgfLPIpHlVVgwGUzgaSaPJZznzCaQrjJRERCUmmVXxzqEI3ugZh8tiwA0L/QwfRBXEbT0WQhbU2GGURZhkEcF4BsmMCrX8RmCdktUowWIUcXA0AaVSXnQFYQAhIioh+4/G8creMYzHs7is2Y2PtnNfD6JK5LbKuHGhH601VjhMEnIqEEpkUX5rYJ1eW60Nh8bTGI9ni10KTTMOwSIiKgGxdA57j8RxcCyBzno7FtTYIHPXX6KK5rHKWNXuBzTgD+8GEIhkoJbhJPTTqXOZYTWKCMazqOJk9LLCx2pEREWUzal4ezCCtbvGkFZyuL7Dh4X1doYPIgJwrCfkps4qLJ3jwtFoBqGEUuySCkYUgAtmOTE4fmwndCofDCBEREUyFstgw4Fx7D0Sw5JGB65occNjNRS7LCIqMQ6zhHuWNaDFb8XRSLrY5RTUbK8ZwVgGQQ7DKiscgkVEVGBpRcXOw1EMhdNwW2TcekE1HGa+HRPR6XmsMm5bUo3+YBJpRYVJroxnyEZZQIPbjKFwGnWuClj+q0KUxSfeyMgINm7ciHfffRfDw8NQVRU+nw+tra1YsWIF2tvbi13ipPHxcWzYsAHbt2/HoUOHoCgKPB4P5s+fj+XLl+PCCy8sdomntWnTJvT19QEATCYTPv7xj8Nk4psB0dkYiaSxtX8CyUwOV7R40OgxF7skItKJRQ129IwmcDSSwWxvZbx3iIKARo8ZOw5HsaTRAUnk8NRyoOsAkkgk8K1vfQtPPfUUxsbGTvk9RqMRq1evxsMPP4zW1tYCV/gBVVXx3e9+F0888QSGh4dP+T2CIODmm2/GQw89hCVLlhS4wg+3ceNGXHPNNVCUY2NPBUHAyMgIqquri1wZkT5MJBVsPhiGoqpodJvRXGXlhoJEdFasRgktfisOBBJo9JorZkWsaqcRqqahL5jEvCouSV4OdNt/19/fj+uuuw7f//73Txs+ACCTyeCZZ57BZZddhnXr1hWwwg+MjY3h5ptvxje/+c3Thg8A0DQNa9aswRVXXIGnn366gBV+uD//+c+49dZbJ8MHAHg8HghCpbz1EZ07DcDBsQTWdAWQyqpYOseNJbOdDB9EdE7m11gRjGUQilXOnAirQYLfbkTXUKzYpdA00eUnYCwWw+rVq7Fjx46844sWLUJHRwdkWUZvby+2bNky+bXx8XH89V//NTZt2oQLLrigYLUqioJPfOITeP311/OOL1iwAIsXL4bZbMbBgwexefNmqOqxFR5SqRTuuOMOVFVV4ZprrilYrSfSNA0/+MEP8OCDD07WRkRTdzSSwY6hKCJJBRfOcnJpXSI6bx6rAbO9Fuw/GscVdnexyykIQQCafBaMRNKIp3OwmaRil0TnSZcB5Ctf+Upe+HA4HPjpT3+K22+/HZL0wS/l+vXrcffdd0/OW4jH47jvvvuwefNmyHJhXvpDDz2UFz4MBgMef/xxfOYzn4HR+MGa1m+99Rbuvfde7Ny5E8CxIVv33nsvduzYAafTWZBaj/fiiy/iO9/5Dt54442C/2wivcvmNPQHk9jUO44qhxGrOnycZE5E00IUgNk+M9Z3h3DBLEfF3IzXuUyodZpwIBDHhY2Fvy+i6aW7IVh79+7Fk08+Odk2GAx49tlncccdd+SFDwBYsWIFnn/++bx5Cm+99Rb++Mc/FqTW4eFh/OAHP8g79vOf/xz33XdfXvgAgKVLl+KFF15AY2Pj5LH+/n78+te/LkitALB582Z87Wtfw0UXXYQbb7wxL3x4PB54vd6C1UKkV4fGU1jfHcKuoSgunuPCTZ1VDB9ENK2q7EbUuUx451C02KUUjEES4LLIODSeglo5ezGWLd0FkMcffxzacbuA3n333Vi1atVpv7+9vR1f+9rX8o499thjedeYKf/1X/+FRCIx2b7lllvwmc985rTf39DQgEceeSTv2OOPP450ujBrfv/bv/0bHnnkEWzfvj3veEdHB9asWYPa2tqC1EGkRzlVw/bBCF7fH4LZIOKGhX4srLdXzCRRIiocgyRgtteCQ+NJRFOVszFhjcMISRAQjGWKXQqdJ10FkFQqheeee26ybTAY8MADD5zxvHvvvRc1NTWT7Y0bN+LAgQMzUuP7NE3D7373u7xjX/7yl8943u233563WteePXvw9ttvT3t9p2K15q8s4XQ68cADD2Dz5s244oorEI1WzpMWoqnKacD+o3Gs2zOGiaSC5fM9WD7PA4uxMoZFEFFxzPKYYDdJ2DlUOZ/N1U4TzEYRB4PJYpdC50lXAWTHjh04dOjQZPvyyy/H/Pnzz3iew+HADTfcMNlWVRUbN26ckRrf19vbiz179ky229racOWVV57xPEmSsHr16rxjr7zyyrTXdyrpdBqSJOHiiy/Gv/7rv2LHjh343ve+B6fTiUgkwlWviE6QyqrY0B3CX/omMNtrwbIWD+b6LMUui4gqgFESMa/aitFoFrF0ZfSCiALgsxkRTSrI5jgOS890FUBOnBC9YsWKKZ974mpS69evn5aaTmfLli3IZD7oIly2bNlJc1ROZ+XKlXntmQ5L7/vWt76F3bt3Y+vWrfjGN76Bpqamya8xfBB9IKdq2HMkhrW7xpBRVNyw0I/OejuMMv9OiKhwmv1WZBQVfWOpYpdSMI0eM3KaxmFYOqermZF79+7Na5/NDucn9pT09vZOS02ns2/fvrz2woULp3xuS0sLjEbjZIAZGBiAoigzvnJXKe/CTlQqJpIKtg9GcGg8iUua3GipssLApXWJqAhM8rFekMFQEvOrrTAbdPVc+Zx4bQbkVA2HwynUukzFLofOka5+Uw8ePJjXPv4J/Zk0NjbmzXE4cuQIksmZG0N4YsCZO3fulM+tq6vLW3EqEAh86GaLRDTz0oqKHYej2HwwjJyq4abOKrTV2hg+iKioFtbbEU4qGAxVUi+IBYmMiozCPcr0SjcBJJfLYWhoaLItSRJ8Pt+Uz3e5XHn7aYyOjiIcDk9rjccbHBzMax+/FPCZ2Gw2eDyeyXY4HEYwGJy22ojo7IxE0vjzniD6xhJY1GDHde0++O3GM59IRDTDTLKIzno7ekYTyOQq44Z8js+CYDyLYLxydoMvN7oJIIqi5PVY2Gw22Gy2KZ9vMpngcrkm2+l0ekZ7QI5fflcQhLPaTFAUxbwAAhzb/Z2ICiuSUvBmbxhbDoZR7TBi1UI/GtzmYpdFRJSnrcaGRCaHofHCLNtfbE6zBLMsYjTKAKJXupkDkkql8iZ1m0ymkzbz+zCyLMNs/uDGQVGUGbup1zQtb+8Oo9F4VrUCyKsVAJfAJSqw/lASr+0LocphxKVz3ajjWGMiKlEmg4gmrwU7h2KY7TNDqoCFY5r9FhyJpJHNqTBIunmeTu/Rzf+YoihQ1Q+6FmVZnvKqUsCxXoXjJ3GfGBKmUy6XQy6Xm2xLknTWE8gNBkNeu1CbERJVunBCwaaeMP5ycAKd9XZc3+5j+CCikreg1oZsTkV/hayI1eAx48hEGuOJyliCuNzoJoAYDIa8m/hsNgtFmfovXS6Xy+tBEUXxpI33possyyfVms2eXTfhiYHjbIabEdHZUzUN3UcTeH7XKNK5HK5r9+HiJheMsm7eJomogjnMEub4zNg1HEUl7JBhNYqodhgxFuMwLD3SzSeryWTKu6k/cUjWmZw4h0SW5Rm9qTeZPnhims1mz7oH48T5KQwgRDNnKJzG5t4w9h+No63WhhXzvfDZDGc+kYiohCyosUHJaThwNF7sUmacKAiY7bWgP5hATq2EyFVedBNADAZD3k14PB4/q3kRyWQyb9Uri8Uyozf1Docjrz0+Pj7lc3O5XN6qV6IontUk9pnCzQip3ORUDVv6wnh5XxAQgOs7fLhothOSyN91ItIfp1nGLK8ZB8dmbpGdUlLtMCKaznEYlg7pJoCIoog5c+ZMtjVNQyAQmPL5oVAoL7DU1tbC7XZPa43HO3GPkqNHj0753ImJibyw5Pf74ff7p6u0c8LwQeVmIJTEi3vGMDKRwfIWN5a1eGDicCsi0rm2GhtSWRW9Y4kzf7POWY0iGlwmjExUxryXcqKrT9vm5ua89tnsZj4wMJA3DKquru6kid7Tad68eXnt7u7uKZ97+PDhvB6TqqqqvI0Ji4UhhMqBktOw/sA4Xt4bRI3DhFULfWipmpn5YEREheayyKhzm9A9Ei/7fUEMkgi/3Yi+YGX0+JQTXQWQxYsX57V37tw55XN37dqV1+7o6JiWmk5n4cKFee2urq4pn7tv3768VbRaW1t58090nhRVQ99YEuv2jCEUz+K6Nh+Wzvn/27vzMLvr+v77z+9+9jN79oQshJAFjChLCLK1KFqrdYHaYlu1drmtuFS56uV2XfYSteWyIN6xrcUbtRtavaZ6r7kAACAASURBVG/9uQBFBEFQEGRPIITs22T2M3OW73r/cSaTDElIAjPnzExej+vigu+ZcybvCcnM93U+n8/7XSDtnHg3PRGR6WDN3BzVMGFb78xfGWjJ2PhhQp+GEk4r02YOCMD69esxTXOsHe/PfvYzkiQ5oZvzO+64Y9z1pZdeOik1HnT++eeTzWYZGakfBLvvvvsYGRk5oXMnt99++7jrya71RCgAyXQ2WAn59dYByn7MyjlZFralSTnT6v0XEZETlnEtFrWleL67zMLW1Iz+fteWcenKu2zvq9Cm5iHTxrT6E3nmmWeyfPnyseunnnqKBx544Liv27lzJ/fcc8/YdSaTYd26dZNR4pi5c+eydu3aseu9e/ceESyOZmhoiJ/85CfjHpsKAQQUQmT6qQYxz+wd5sEXBrBMg8vOaGP5rOyM/mEsIgKwak6O/pGAnf0zexXEtQ3yKZvdA5qXNp1Mq5/ClmVxzTXXjHvsc5/73HFfd8MNN4ytRAD83u/9HnPnzp3w+l7sT//0T8ddX3/99cd9zYYNG8YdWH/d617H6tWrJ7y2k6XwIdPNnsEaP3ryAM/tL3PGrCyXr2inkJ5Wi74iIi+b55ismpfjmb3D+OHMPguSS1n4YcxgRd2wpotpFUAA/vzP/3zcNqY777zzJW/sv/vd77Jhw4Zxj33oQx+atPoO9653vWtc0Hn00Uf58Ic/fMzn33PPPUcEqpd6fqMphMh0UAliHtkxxANb+unIObx+VQeLO9LNLktEpOHOnJ2lVI1mfEeszpxLNYjZpsPo08a0CyCzZs064ib9k5/8JB/84AfZuHEjvu8TRRG7du3ii1/8Itdcc824ienvec97Jn371UHZbJYvfvGL4x676aab+OM//mMee+wxarUaURSxf/9+vvrVr/L7v//74wYQvulNb+Itb3lLQ2oVmQm291X54ePd7Bmo8tpFRS5Z3kZa261E5BTl2SZnzcvx2x0lwhk8rK+YtmnJ2PQMn/iAammuabkf4aMf/SgPPvgg//M//zP22Fe/+lX+9V//ldNOOw3Lsti9ezdDQ0PjXnf22Wfzla98paG1vvvd7+aBBx7gn//5n8ce+8///E9uu+02lixZguM47Nu3j76+vnGvO+2007jlllswzalx82QYhlZAZMoaqoQ8113m+e4y81o8XnOauluJiAAsn53lhd4Kj+8qcc7C5g81niyLOzLsHahRC2I8vfE05U3LAALw7W9/m87OTr72ta+NPeb7/jHnbVxxxRXceuut5HK5RpU4ZsOGDbS1tY3bKhZFEZs3bz7q888//3z+4z/+g1mzZjWqxBMSBIda3Pm+T5LM3HdTZHqIE3hyd4ln9g6zoDXF61e105pRFxQRkYNStsnquTl+uWWAlXNyM3ZVeG7RY0dfhb5ywJyi1+xy5Dim7Z/CVCrFhg0buO2221i/fj2ed+QfNtM0WbNmDTfccAO33347c+bMaUKl9dWDz3/+8/zkJz/hd3/3d0mnj74f/YwzzuBzn/scd9999xFDF5vNMAza29spFosUi0U6OjqmzOqMnJoODPvc81wfT+8Z5qx5edYtbVH4EBE5igWtKbryLr/ZPtjsUiZNMW3jhzF7BtUNazqYtisgB1111VVcddVVbN68mY0bN9Ld3U0cx7S2tnLGGWewatUqLGtqbMW48sorufLKK9m6dSvPPPMM+/fvJwgCisUiy5cvZ/Xq1biu2+wyjyqVSnHXXXeNnacxTXNKTGeXU0+cwINbB9jWU2F+S4rfP7uTnDftv5WJiEwazzZZNSfHPc/2sWJWls781LzXeKVaMg77h2r4YYxr603SqWzG/NQ+/fTTOf3005tdxglZvHgxixcvbnYZJ02BQ5pt32CNJ3YPc2DY59ULCpw55/iDPUVEBOa3eMxr9XhqzzCXnjEzf57PKXhs7SkzVA3pyM3MkDVTKB6KyJRX9iPu3dzPL7cMUEjbvPmsToUPEZGTYJoGq+bm6S8H7BuamduUFrSliBODAc0DmfJmzAqIiMw8cZKwb7DG47uHGSwHnLekhcXtmukhIvJyzC64zC56vNBTYVbeY6Y1t/Rsk8UdKUrViCRhxn19M4kCiIhMSYOVkAe3DjBcCTlrfp4Fra2k3alxnktEZLpaMzfPnc/0sLO/wsK2mfeGzqK2NM/tL1MN4xnb8Wsm0P8ZEZlSwijhue4R/veZHsIo4ZIV7SyflVX4EBGZAPmUxbwWj0e2D1EL4maXM+FmFTx6hn2qftTsUuQlaAVERKaM7pLPQ9sG8cOYlXNzrJzT+Lk9IiIz3Zp5eTYfKPP8gTKr5s6s77OWaZBP2QzXIlqzas0+VWkFRESaLogSntozzM+f7cMxDa5Y2aHwISIySbKexbolLTy+q0SpOrMObDumwayCy/6S3+xS5CUogIhIU23vq3L70z30DPucs6jAFSs7yHnabiUiMpkWd2RIuxZP7h5udikTyjBgXjHFjr4KYZw0uxw5BgUQEWmKsh/x8LZBHtjSz9yix7qlLSzrzKhriYhIA5gGvGZRgZ39VQZnWNvaXMqiGsQMlINmlyLHoAAiIg0VJQlP7C7xk6d6GKiE/M6Z7ZyzqIBr6duRiEgjzWvxaM+6PLJ9qNmlTCjXNmjN2tqGNYXpJ76INMxAOeT+5wd4aOsgK2ZnufSMNjo1rVZEpClMw+Cs+Tl6RwJ29FWbXc6E8WyL2YUUvcMKIFOVumCJyKSLk4Qndw+zvbdCyrH4g1d10a7gISLSdF15l9kFl99sH2RO0cOxpv8+WNOA1ozNlgPlZpcix6AVEBGZVH0jAT/b2MeTu0ucOSfLFSvbFT5ERKaQ1ywqkABP7C41u5QJk3JMUo5Jqap5IFORVkBEZFKU/YjfbB9iqBowu5Bi7cI8HQoeIiJTTtq1WD03x1O7h1nelSGfmv63h4WUTc6z2DtYJZ/KNrsceRGtgIjIhNs/HHHPC2UqicP5y7p4zaKCwoeIyBR2xqwsLRmbp/eONLuUCZFxLfKezfYZdLZlJlEAEZEJ018O+dWOMvc+ewBjaC+Lkt3Eg3sB9WIXEZnq1szLs3HvMPuHZsbh7bRrEkQxiX4ETTnTf41NRJouThJ29dd4eOcIu3dsY+v9/y+P/vJuypUqbirNW9/6Vt7//vfT0tLS7FJFROQYuvIui9pTPPBCP7+3pmvaH0hvzTjsHfTpKwe0Z51mlyOHUQARkeN6cvcwCQlLOjJHTCnfO+izcV+JSmSRjwb49W1fZs/OHbztnVexeMlSHn7oIb71rW8xMDDAJz/5STKZTJO+ChEROZ7zTmvhB09089z+EVbNzTW7nFekNePghzF7B2sKIFOMAoiIHNNz+0f45oN7+enTPVgmrJid5XXL2viTC+bg2SbP7B3mid3DzG1JcemyFv7PbT9iy+bNfP76L3D55ZcD8PtvfjMLFy5kw4YNXH755Vx22WVN/qpERORYsp7FBYtbeHxXicUdaTKudfwXTVEpxyROEro1kHDKUQARkaN68IUBPvmD5/n5pj4c28QyDX6zbYjvPtLNM/uGuXJ1O65lct5pRRZ3pCGJefKZTSxZuoxzzjln3Oe64IILuOWWW9izZ0+TvhoRETlRC9tSPLtvhIe2DXLJ8rZml/OKzCq6HCgFBFEy7beUzSQKICJyhHItYsM9u/j5xl6KWQfLMEiAtGNSDWP+9b5dnDErw3vWzcOz670s4gTe8573EMcx+Xx+3Od7+umnCYKAefPmNeGrERGRk2GZBmvm57lvcx+7BqrMb0k1u6SXrTPrsm+wxmAlUDfGKUQBRESOsGn/CE/sLmE71lj4OMixDMIoYWd/bSx8AJimydKlS4/4XNu3b+cb3/gGq1evZu3atQ2oXkREXql5LR6ndaT59dZBZp/tYZvTc/WgmHawzfqbZzJ1qA2viBxh31CNUjXEtYwjGugao//sG6zRXw4IomN/U9+xYyef+MQnCMOQj33sY+qCJSIyjayZm8cxTTbtm76zQVoyNlGc0D8SNrsUOYxWQETkCB05l4xnEQwkpOzxUzwSIErqKyGP7BgijBLyKZusZ5F1TdpyKYoZi8d/+zhf+NxnqNWqfPmGf2DNmjXN+nJERORlyHoWa+bl+NXWAWYX3Gm7hSmftqmGUbPLkMPMyADy3e9+l97eXgzDoLOzk7e97W3NLmnM3r17+fnPf86jjz7Kzp07CcOQlpYWli9fzute9zouuOCCZpc4pre3l3vuuYeHH36Y7du34/s+hUKBZcuWsX79ei6++OJmlyiT5JyFBS5c0sLTu4YJRg+gY0CSwEgtZllXmnefP5eFbSn2DNYYqoSUqiGJYZEZjnn8/rv4f27+EqFX5LN/fz3zzjyH3pGAfMrG1SFAEZFpY3FHmue6R/jtjhKXndmGZUy/7+Fzix67+quU/Whad/WaSWZcALnxxhv5yEc+MnZ9+umnT4kA4vs+n/3sZ/n6179Ob2/vMZ932WWX8fd///esW7eugdUd6frrr+fmm29m3759x3zO+eefz2c/+1ne8IY3NLAyaQTLNLju9YvZ0V/ljqd7cCwT0zSoBTFLOtN86Q9O58Kl9e1UC1oPHU6MgH//7g/51y9/ngsvOJdr/q/rKMxayMY9JQYrAaVKQNY1aM86tGYc2rMuLRkb1zKZhj/TREROCeuWtnDXM73sHfCZ3+o1u5yTlnEtStWQahArgEwRMyqA3HDDDXz84x8f99hU2HO+Z88e/uiP/oh77733uM+9++67uffee/n617/Oe97zngZUN16pVOKaa67hhz/84XGf+6tf/Yorr7ySf/zHf+RjH/tYA6qTRlramebr717JfZsHuOe5PnYP1FjSkeaPz53DeYuLR33NU4/9lv/++s28+Q2X86lPf4ZsNksURZQLKYZrLv3lkFoQURkdDLWjr0rvSIBhQFvGoT3r0JZ1mFWoH3g0DLBNo74CIyIiTZH3bFbMyfHAC/285eyucQ1IpoP2rEMliCn7EW0aSDglzIgA0tvby3XXXcc3vvGNZpdyBN/3ecc73sGDDz447vGzzz6bs88+m1QqxZYtW7jvvvvw/fqgnCiKeO9738ucOXMavrrw7ne/+4jwceaZZ3LOOeeQzWbZtWsXv/jFLyiVSmMf//jHP05XVxd/8id/0tBaZfLNb0nxrtfO5s1ndVDxY/Ipm5Rz9B88lUqF/3vD1/jFLx8gwuADH/gAtVoNg4QoDMll0/z1X/01rzl/HUkClSCiFsaM1CLKfsxQNaDix2ztqfDw9iFMAxzToDVr05px6cy5eE59O1hqdFuYerqLiDTGss4MLxwY4ZEdg6xb0trsck6KZ5ukHItKoE5YU8W0DiADAwN897vf5frrr2fbtm3NLueoPvOZz4wLH6lUin/7t3/jXe96F6Z56Ebuqaee4r3vfS8PP/zw2GPvf//7efLJJxu2inPTTTfxgx/8YOzaNE1uuukm/vIv/xLHOfSOwZYtW/iLv/gL7r777rHHrr32Wi677DLmz5/fkFqlsXKeTe44q+6lUgnLsjjvvPOwbZvdu3ePfcz3fbLZLKVKDQDDqC+JZ1yL1syhP1thlBDGCdUwJghj+sshQ9WA4VrI5u4yUZxgmwZtWQfPMWlNO6Qck5RjknEtbNMg62l5XURkojmWwQVLWvnZpl52D9SY1zK9tmLNLboMVELiOMHUqnrTTbsAkiQJ3/ve97j99tu588472blz57iPz58/n97eXiqVSpMqPGTXrl3cdNNN4x679dZbufrqq4947urVq/nRj37E+eefz9atW8def+utt/LhD3940msdGhri+uuvH/fYjTfeyN/8zd8c8dylS5fywx/+kPXr1/PYY48BMDg4yM0338yXvvSlSa9VpqbOzk5uvvnmo34sSRIMwxgXuo/GtgxsyxhbZenM1zuuxElCFEMQxQxVQ/pHAmphzNbeMpUgJmWb9dURA9KuTco2SbsmhZSNa5sU0/a07WEvIjJVtGUdlnVleHjbILPP6pxW22MLKYfN3SPUoixpU29UNdv02sRH/Ubmb//2b7nllluOCB9vf/vb+da3vkUcT40ltltuuYVqtTp2/ba3ve2o4eOgrq4u/uEf/mHcY1/72tfGfY7Jctttt9Hd3T12fdFFF/HBD37wmM/PZrN85StfGffYrbfeSn9//6TVKFObYRjYtn3UfxzHwbbt4waQYzGN+narjGsxu+Bx5pwcr1pQ4I2rO3n72llcfHorp3dmmNeaIoxidg9W2dZb4andJX69dYB7n+vj3s19/Gb7IFsOlNk9UGW4ppaMIiIn64xZWeIk4aFtg80u5aQU0jZ9IwF++OLpVtIM024FBCCfz4+7XrJkCR//+Mf5q7/6KzZt2kStVmtSZYccXKk53OHduY7lbW97G2eeeSYbN24E4LnnnuPhhx/moosumpQ6D/rOd74z7vpEVl0uuugiLrroIu677z4Auru7+dnPfsY73vGOSalR5FhyKZtcqv7tbHlXFoAwTugfCRiuRZSqIYPVkMFKRO9IQBTXfwAlCaRdk7aMQ86zyadtOnMOplpyiYgcVca1OGdhgXs3D7CoLc3cabIVqy1j49kmgSaiTwnTLoBEUUQYhrS2trJ+/Xre8Y538Pa3v51stn7TMTIyNaZ1bt68mU2bNo1dr1y58oRa65qmyVvf+taxAAL1zliTGUB6enp45JFHxq7nzJnDFVdccUKvfec73zkWQKBeqwKITAW2adCZd+k87P2KJIGhakAtTCj79WBSCWIGyiE7+6uM+BGmYZB2LFrSNu05h6xr0ZZ1ce16Ny7LMNQyWEROaYva0yztr/HojkHacx3ToiuWaRi05Rx6ywEd+ek5UHEmmXYBBOC//uu/mDdvHl1dXUd8zJgidwa/+tWvCIJg7PrCCy884e0nF198MV/4whfGru+///4Jr+9wjzzyyLitU+eeey65XO6EXrt+/fpx1w888MCE1iYykQwDiukjWzDWwphqEFMNImphMhpKAvYN1tg35FMLYwppm47RQOLaFm2Z+vR3zzZxLFMduUTklPKa0wr8f4918/iuEueedvTW7FOJZRpkXYsDJZ/lXZkpc794qpp2AcRxHNauXdvsMo7r2WefHXe9cuXKE37tkiVL8DxvbCvZ9u3bCYJgXCeqifTiWs8888wTfu3ChQvp6Oigp6cHqM886e/vp7V1erXok1ObZ5t4o4fVD0oS8KMYP4ypBDFRnDBQCRkoB2zvG2GoEpJxLQopi3zKppCy8RyTtFPv7pVyTLKehX7EichM5NkmFy5t4VdbB1jSkaYjN7VXFQwDWjMOuwaqxAnoPaPmmnYBZLp4/vnnx10vXrz4hF87Z84cWltbx6aQd3d309PTw5w5cya0xoNeXOvSpUtP+LXt7e3MmjVrLID09/ezd+9eBRCZ9gzjUDDJjw57n1P0iJOEeLQjVxDD/qEaI7WQ/SWfnmEfMMg4JrmURdazSdsmjl3f1pXzLAppe1psVxAROZ75rSnm9qf45fMDvGH11N+K1ZFz+M32IcIomVYdvGaiGRdApsqS2os7dB1tu9ixZDIZ2traxgLI4OAgvb29kxZAduzYMe76ZGoF6OjoGPvvMAzp7u4+qRUfkenENAxMC2zLIg0UUplxH68EMT3DPtUgpnfEZ/dgjThJCKOEtGPi2ibm6BDFQsqmJWOTcy1aMprOKyLTz6sXFPg/Txzgmb3DrF1QaHY5L8kyDaI4ob8SMtuZ2is2M92MCyBTxeFzSAzDOKJz10sxTfOI4YPDw8MTVtuLlcvlcdfF4snt5Wxraxt3PZm1ikx1acdkQWt9yeR06uEkSWCgElD2I4YqIT0jAbUgZne1yr5BgyhJCKIEz6535CqkLTKuzeyCq3fpRGRKSzkm65a28Mst/cxvSY3Nb5qKHMukNWszojbsTTfjAshUWAFJkmRcK2DXdfG8k2tTl06nx11P5k39i4c2vtJaS6XSK65JZCY5uPe4NeMw77D3FipBTNmPqAQRfSMBQZQQhDFbe3z8KMEPYyzToCVt05Jx8GyTOUWPtGNiGPWuXMonItJs81o85hY97t3cz1vO7pqyTTlSjklXzqNUC5tdyilvxgWQqSCKIqLoULq2LAvLOrmpm7Y9/n+N7/sTUtvRHN6t62i/9vG8+PlTYQ6LyHSQdkzSjgk4zG+pr5pEcb1FsB8llGsRtSim4kf0lwPKfsxTe4aJ4oR8yqI14+BaJrMKHhm33onLtU0s09DkdxFpqHMWFvnxUwd4dMcg5y1uOf4LmsAy62/c9I1M3j2VnJgZF0AMw8AwDJKkeZMuD05/PigIgiNu8o/nxTfxmUzmGM985V684nGyYefFtR6cySIiJ88yDfKjQxXbs4fOhfhRTBQl+FFCLYzpHQkYqgSUaiE7d1Sp+BEpx6KYtjAwaM86FDM2rm2ScSzSjoVrK5SIyORIuybnLS7y82f7mF3wWNSePv6LGswA2rI223orx32uTK4ZF0CmilQqNfbfQRBQrVZP6vUvPpdxonM5Xo4Xb6E62VpfPPxxMmsVOVW5lgkWHPzb2pV3SZL6ls8wSYiihL5ySN9IQM+wzwu9FcLuBMuEjGPh2ubYVrB8yiLv2fVWwa7NCY4oEhF5SQtaU6yYneXRnSVmF70p2RXLtgz8MKYWxlOyvlPFjAsgU+EMCHDEofOBgYETfm0YhvT19Y1dW5Z1UofYT9aLP3dvb+9Jvf7Fz3/xAXoRmRyGUf+e52KABfNci3kth1Y0E2CoUg8lvSM++4Z8+sohnmVgmmBiUAsT8mlr7JxJIWXTlnE07V1EXpbXLCqyZ6CbR3cMccGSqXc/kHJM8imL4VqkANJEMy6AAE3fggVw2mmnce+9945d792794RfOzg4OG4yeUdHB52dnRNa3+GWLFky7vpkag3DcGwGCNRXU2bNmjVhtYnIy2cAxbRNMW2zuOPQSmfFj+kb8SkHEd2lgJFa/RD8vqEatmlSCWJIEvJpm46sQzHtMLvgaQuXiByXacB5i4vc81wfC9vS494UmQoyTn1QbP9IMG6bqzTWjAsgU2UFZNmyZeOun3vuuRN+7a5du8YFkM7Ozkkd7PfiWl88Gf2l7Nu3b1xgaWtrY/bs2RNWm4hMvLRrMs8dbRU8OvYnSWCgHFANY/pGAoZrEdUgZt9gjf1DPo/uGALAtQxaMg4tGZs5BY9sqn7mRB25ROSgOUWP1XPz/GJzH28+q5OcN3VuN127viW1HKgVbzNNnT8RM8yaNWvGXT/xxBMn/NpnnnmGOI7HrlesWDGpwerFtT755JMn/NrNmzePaxG8bNmyST0wLyKTwzCgdfTdwDnFQ+9Ylv2IIEoYqoZUR9sGD1YCDpR8nts/QjVMcCyDzpxD1rNpz7oU0xaWaeBaJrZpYE/RlpwiMnlWzsmyZ7DKr7cOcvmK9maXM8azDVK2yb4hn7PmNbuaU9eMCyAHu2A12/nnn08+nx+biXH//fdTKpVO6CzHT3/603HXl1566aTUeNBZZ53FggULxqa3P/LII+zevZt5847/N/OOO+4Yd33xxRdPSo0i0hwZt95CvJg+9OMijBKiJKESxPhhzIgf0T8S4IcxT+8tMVgOybgWhbSFbRoU0w4taRvHrrcd9uz6PxqyKDJzWabB+qWt/PTpHh7bVeJV8yfvLOvJ8hwTf+TkupPKxJpxAWSqmDVrFq9+9avHzoHs37+fH//4x/zhH/7hS75uYGBgXAAxDINLLrlkMkslk8mwbt06brvtNqDeget73/se11577Uu+zvd9vv/974977PLLL5+0OkVkarAtAxtj3AHOxe1p4iQhiutT3atBTO+wT3+l3pVrc3cZyzTIuiamYZByTArpepvgvGePzURJuyc3M0lEpq6sZ/Gq+Xl+uWWAeS0enbmpMSU941oYQBAlU3Zo4kw34wLIVFj9OOjP/uzPxh1E/8IXvsDVV1/9kjV+5StfGXeo+9JLL2XlypWTWifUaz0YQABuuOEG3ve+973kTI9vfvObbN68eez6rLPOYt26dZNap4hMXaZhYFoGjlX/Ad922AHPJIHa6PmSvnL94PvOvipxUt/+5ZgGUQJZ1yLjmuRT9a5cOc8i5ymUiExXy7oy7B6s8cvnB3jjmo56S/EmSzsWGDBcC2nN6CB6MzT/T8EkmCoh5Oqrr2bBggVj10888QR//dd/fczn33HHHVx//fXjHvvIRz4yafUd7vWvfz1r164du965cyfvfe97x51FOdxvfvMbrrvuunGPXXvttSc9RV1ETg2GUW9/ObfFY/XcHOctLvKmNZ28aXUn551WZMXsLPNaPBISStWIbb0VHt81xO1P9/D933Zz96ZeHto2yJYDZbpLPn7U3E6HInLiXruoQELCk7uGj//kBkg59ZXYsn/0exyZfDPubnGqhA+ot6S94YYbuPrqq8ce+5d/+RcOHDjAddddx6pVq3Ach/3793Pbbbfx2c9+dtxU8T/4gz/gTW96U0NqNQyDL3/5y1x++eVjoeM73/kOfX19fOpTn+JVr3oVqVSKnp4efvCDH/DpT3963GyT9evX8+53v7shtYrIzGGa0Jmvb8tYdNjjI7WI4VpIfzlksBKSAGU/ZqhSob8cUA1i8imbjpxDS9oh49VnmeRTNqYBpmkwdX4aiJzaMq7FhUtauXNjL7MLLvNaU8d/0SRKOSaWARVfnbCaZcYFkKnmqquu4sEHH+TGG28ce+z73/8+3//+91m4cOFYADm8kxTA8uXL+frXv97QQHXJJZfw+c9/nk984hNjj911113cddddzJ8/H8/z6OnpYXBwcNzr5syZwze/+U1cd2rs7RSR6S/rWWQ9i1mFQx25wjih4kcM1yJK1ZAohoFKQM9wjf4DEf3lgLRj0path5L2nEM+ZZOyTRzLwLZMXO33FmmKroLLqjlZHto+yO+Obq9stRCY7AAAFspJREFUloxjAQZD1bBpNZzqZmQACcNDf6B8329iJXX/9E//RHt7O5/+9KfHPb5jx46jPv/iiy/m29/+Nu3tjW9b93d/93fk83k+8pGPEASHOkTs2rXrqM9fu3Yt//7v/37EMEMRkYlmmwb5VH2V42Cr4DhO8KOEIIop+zFBnDBYDhiqhLzQU2b/oE8C5FMWrWmH1qxDa8bBsQwcyyDjWjiWqYOoIg2wdmGBnpGAB7b0c/mK9qZ1wrOtehONUk0rIM0y4wKIbdvMmTNnLIRMlancn/rUp7jwwgu58cYbuffee49YRbBtm1WrVnHNNdfwoQ99CMdp3qGoD3zgA7z2ta/ly1/+MnfddRe9vb3jPm6aJitWrOCqq67iox/96Am1FhYRmQymaZAy61218qO7Oua3eMRJQhgnhAc7co0ElGshpWrI891l4iTBsUyKaRvXrgcb1zJxbZNi2sKzdfhdZDKcv6SFHz3RzcZ9I6yem2taHZ5jUAt1lqxZZlwAWbZsGRs3bgQgSZIpdSj60ksv5dJLL2XPnj1s2rSJ7u5ufN+npaWFpUuXsnLlyilzhuXcc8/lv//7v+nu7mbTpk3s27eParVKoVBg8eLFrFq1akr93oqIHM40DFzLwD1GRy4/ihkoBwxVI0b8iN0DVYIQEhIyjollGTiWiWuZ5FIWxZSNZ5u051xNfBd5BfKexUWnt/Kzjb105BxmH7bNspHSjkU1CAnjBFt/qRtuxt1BmqZJsVhsdhkvae7cucydO7fZZZyQrq4uurq6ml2GiMiEMQzwbJNZBY9ZhfpjB4ekDdciBsoBtTBm31CN4dpoQOmvEkQxlmViGQb5lEVbxsGzTTpyLlmtloicsHnFFKd3Zfnlln5ev7KzKauNWc+id6Te0EKrnY034wKIiIjIy3X43JGlnRmgPvl9oBJQCWJK1ZARP6Lsx2zvq4eSKE4I4/pr27MOKcekPevSlnUwDLAMg0YsbiejM1VEpjrDgAuWtnD70z3c/3w/b1jV0fAaXMugGkQKIE2iACIiIvISbMug40UTnOMEyn5IECaUg3ogKVXrrYN39ld53C9hGAZpx6Q1Uz/4nnEt2rN2fZK8WT9v8kp2fsRJfep82a/fRGVci0LKVgiRacEAzjutyO1P9/DUnuGGnwfJujaWaeBHmgXSDAogIiIiJ8k0IOfZ4EErh86X+FGMHyb4YUwQJQzX6u2B9w/V2DNYI0nqWz/ynkXLaDeutmw9nKRdE8c0STlHzgiOk4RaGDNcjaiFCT3DPrsHqhwo+QRxwrolLXTlXYUPmVbasg5rF+b59dZB2rPOWHe7Rsh6FnEC1UCdsJpBAURERGSCuJaJawGjWzpmAUmSJozrrYL9MMGP6l25Bso+W3t8nt4zTMazyLomnm3hOaMhJDm0rapvJKC/HNAzHDDiR0RxQjWIWNCa4g0rO5jX6mEqfcg0dObsHBU/5r7n+7lyVQf5VGNuTVOOSXV05VIaTwFERERkEhkGo3NHLDKjO7m68i6QJUkgjGNqYcK+wRqVIGb/UI0Xuit0D/s8vqtEGMWkXQvLrM8usE2DrGdxweIWXre8pb4SIzKNrV1QYO+QzyPbh7jkjLaG/bpp1yRRJ96m0HctERGRJqmHExPHgmVd9UPva+bV98JXg5jfbB/kgRcGeHZ/mSBKCKOIME7IeRauY/DojhJJktCWdSikbVrT9X+LTCeGAZcsb+V7j+7nyd0l1sxrzHwx1zaohjoD0gz6LiUiIjIFpRyT9ctaWT0vzzN7hnl8V4nuko8fxbRkbAqp+iHaih+xv+TTXfKphfVtXq5tUEw7tGdsCmmHzryLaYJJYzpyiZysrGtxwZIiD28boi3rMK8lNem/pmdbVEa3NDZrKvupSgFERERkCmtJ26xb2sKyrgy/3jrIU3tKDFUj+sshr1/ZDkAliPDDhBE/olQNqQUxpVrI/mGfbX1VBishnm3QmnXIuTZZz2JWwR1dfTGwTKNh7YJFjuX0riyDlYhfbO7nrWd3kXYntz1uzrMYjBOiBNSIt7EUQERERKaBrrzLlas7WNiW4v7n+3l0xyAtGZvzTiuSdizSDhTTNox2EoriepteP4op1yJqUUKpGtI34tMz4vPE7hJQn0ydT9l4jklXzsVzTDzbJGWb2KMBRaRR1i7IM1QJ+eWWAS5b0f6KWlUfT86z6BsJ8MMY11IEaSQFEBERkWnCNg3Onp9nWWeG3+4c4oUDZRa1pZhdOLJ9qWXWVzZSjknhsM5CUZwQjc4QqQUxB0o+pVpI2Y94dGeJIIpJ2SYZ1yIB2nMuudE2wTnPIuNaePaRrYJFJoJlGpy7uMidz/Tw0LYBzj2tOGkd3lKORRglhFGM1kAaSwFERERkmsl6FuuXtbJ3sEbtJA/RWqaBhYFr1ffdt2UPzTGJk4QwSugZrrf97R0J2DNQJYrrrYLSjkWcJJiGQVvWJu1YFNM2+ZRNLmWjxRKZCDnP4oIlLfz06QN05V2WdGQm5dexTYM4SYjVCavhFEBERESmqYke3GYaBq5tMLfFY27L+M89WAkZqAT0DgccGPY5MBwAAWY/BHFCNYgppGxaM3Z9+nu2PgFeZ3vl5ZhT9Dj/tBYe2jZE3nPozDvHf9FJynkWfpScdIiXV04BRERERI6rmLYppm0WtaXHHvOjhKFKwEAlHNtLP+JH9JUD6IGhSoRp1g/Sd+TqgWR2IYVtGRigQ+/yklbOzdFfCXnghX7euLpzws8jZVwLP4zxIwWQRlMAERERkZfFtQw6ci4dORc6649FccJwLaISxAxWAip+PZT0jgR0l3x+vXUI06jPYGjLOrRmXGYVXFKOiQk4tolpoMnuAsAFS1r48ZPdPLCln4uXT+yQQsOobzuMlT8aTgFEREREJoxlGqOrJTC7UB/9ngC1ICaIY0Zq9ZbBg5WQUjXkwHCNZ/YOE8YxjmXSmXPJOCZtOZesa2GZ9bMntmXo8PspyDTgotPbuPOZHp7YVeKs+RM7pDDlmEQah95wCiAiIiIyqQzqN3opTPLeoVuPOEmIYsa2wZSqEf3l+laup/YMU/EjTKO+/SvtWuQ9u96FyzHq0+Atk6xn65zJDNeStnnNoiK/eK6flozDwraJG1LoWKZWQJpAAURERESawjQMTAscyyKLRethN5dxkhDGCSO1iJ5hn1qY0F2q8UJPhGsbeJZBnNT38addk9RoR66UbZJL2aQdrZbMJEs60gxXQ+5/vp83rOoY173tlXBtk5FaNCGfS06cAoiIiIhMOaZh4FoGbsakNXPwZjMHQC2M6R0OGKoGlKoRfSMBUeyzE4OEBMs0sE2DlGPRmrHJeRY5z56wm1ZpjrPm56mGMb/aOsDlK9onZEueaxkEUUycoJW0BlIAERERkWnFs816q2AOtQqO4oSBckgliBjxIwbKIbUwZvdAjWoQUfFjXNsk7ZgU0zYdOZesZ9OWtXEsUzef08TaBQX+d2MP/7uxl9ev7HjFnbFc2yCIEpIkUVu2BlIAERERkWnPMg3acw4wfpWj7EeU/XooqfgxtTBmqBqyrbdCd8nHjxKKaZvOnEM+VR+u2JZ1yLgmjmVimYbCyRTiWAavO72Nuzb28OTuEq9eWHhFn8+1TCp+SJxoFnojKYCIiIjIjJVxLTKuRcdhj4WjgxMrQUS5FhElMFwNGayGPLe/TKkaknEtWrP12SetGYeMa5GyTTzHxLVMUjpj0jQ5z+J1p7fxkycPkPUszpiVfdmfy7ZM/CgmjhOY4DkjcmwKICIiInJKsU1j9FyIdfBYCQkQRglhFFMNY8IYuks1RmoRW3sqHCj5GIZBPmVSSDm0ZGyyno1jGniOScYxyXk2tm5iG6It63D+0hbuebaPtGO97M5YKdvENOpnh6RxFEBERETklGdQ397jWBZpt74ZpzNX385VbxecUPZjeod9KkFMXzlgW28Vkvqta9azyLoWnm3i2CZZt96VK+2Y5FO63ZoMyzozdA/5/GJzH285u+tl/T7X2/AmqBNvY+lvhIiIiMhLqLcLNiim6wfYDxdECf3lgJFaxFA1YP9QQBQnxEmCaxsYGBhG/eB8MW1TSNfPmXTl3SZ9NTPLuYuLDFVD7n62jytWtpN2Tu4kh2ub+HFS34IlDaMAIiIiIvIyOZZRDxN5gPTY46VqxHCt3pXrQCkgiGL6RkL2D/nESUItTLBNg2LGpjXt4DkmswsuacdSM6aTYJsGl69o5+fP9vLI9iEuXNp6Ur9/rm0QRgnKH42lACIiIiIywfIpi3yq/m78ktET8EGUMOJH+GFMfzmgFsT4UcLewSrVMObxXTFRDIVUfWaJZxvMKnjkPGtstolpGiifjOdYBhed3sqPnjjAw9sGOXdx8cRfaxqUqiGREkhDKYCIiIiINIBjGbSMbuE6fAtWxY8I4oSKH1MNIvrLIcO1kIFyxJYDZSpBTNa1aMk42JZBe9ahLevgmAaubeLZ9XbBp7K0Y3HRsjb+d2MPaddizbzcCb3OtkZXQHQIpKEUQERERESaKO1apIHCaCOnRe31jlxRkhBECUEU018O6S8H9A777B6oEUUxjm2S92yyrjUWTNKuRdYz8SyTlGudUqsls4sul61o5xeb++jKu8wqHP+cjWHUQ5y2vTWWAoiIiIjIFGNbBjYGng1g0ZpxgDRxUn+3Pk4ShqoRvSM+fSM+u/prvNBTJufZpF0TA4hixg6+F1IWOc8+4hD9TDOvxeP0rgx3P9vL757ZTkfupUPIwdyhLViNNbP/FIqIiIjMIPWOXAAGHTmTjpwD1AfxxQnURlsED5QD9g3V2DdUo28kICHBs02i0a1G+VR94nshZdOVd2fUFq7XLCpS9mPufa6PK1d3knFfujNWGCf4oQJIIymAiIiIiMwApgFp12Se6zGvxWPV3Po5iChO6Bn2qQYxB4Z9hqoRQ9WQgXKAbRr4UYJh1FvStqRtWjIOs/Le2CH66Wj9shbu3tTHLzb38zsr2o85INIwIOWYGBps31AKICIiIiIzmGXWu2kBLGqvtwqOExiuhvij50vKfkQ1iBmohAxWQp7ZM4wfJTiWQWfOJZ+yaM+6tGRsLNPAMo36aswUXTgxDYMLl7Vyx9M9/GJzP5ee0XbMcx6ubaL80VgKICIiIiKnGNOAwuh5kMPPSdTCmCBKKPvRWCgZHA0l2/uqDFVCMq5Fa8bGc0yKKYeOXL07l2uZo9Pkp8ah7rRjctkZbfzk6R4efGGAdUtbjvo8TUJvPAUQEREREQHqE9s9G3Leoe1XcVLfxuVHMbUgphrG9I8E9JcDdvRXeHxXCccyyKdtXMsg7Vi05xxc2yTjmKQcE8eqtwtutELa5vUrO/jfjT08tWeY1XPHt+c1AAODih81vLZTmQKIiIiIiByTaYBpGTiWRXb0QPfcokeS1LtxHTzE3V8O6Bn2GfEjntpdIk7AtUw8x8S2DAqejeuY5FyLQtom41qknckPJa0Zm9cuKvLzZ3vJe9bYNjSot+FNuyYvp1/x5s2bcV2XRYsWTWC1pwYFEBERERE5aYYBllE/D+LZ9c5aC9tSYx/3w/rh9+Fa/YxJz0hAbSghoX62JIwSHNsk65rkPJu2jEMuVW8VPNFnSxZ3pBmoFLhncz+XmwbzW1OjX0NSD1gn+ett2rSJT37yk7zxjW/kfe9738QWewpQABERERGRCefaBnNbPMAb9/hgJWSoEjJQCRiuRQRRwv4hn/1DNYZrMX4YU0jbdOQcWtI2GdemJWOP2xb2cqxdUCBO4N7N/VxxZjudeRfbNOvT5K0T/9xbtmzhM5/5DI8++ijvfOc7X1FNpyoFEBERERFpmGK6vsqxgPGrJaXaoWASxVD2I3ZUqgzXIgYqAVm3frakNe3QnnMppm0828Q2DUyz3vnqeM5ZWCAIEx7ZOcRrFha47/kBfvrUAS5Y0srbX91FIXXsW+MoirjjjjvYsGED+/btY9asWRhT4bT9NKQAIiIiIiJN5doG7bZDe7Y+8R0gjBKqYUw1iBiuRfhRwkg1ZKga0j3s010KIEkoZurbt9qyDh05F8cysE0DzzbHWgYf7rzFRf7lvp187kcvsK2nQiWI+fGTPWy4dycfuXwhf3TunKPW+NBDD3HDDTdw9tlnc+2117JhwwaCIJjs35oZSQFERERERKYc2zLIWRY5z6LjsOZVfhTXw8loR66Bcn0r14GSz1N7hoH64fe2rI1n1ye+u3a9PXBbxuGZvcPc+sBefr11gLRr4VgG/ZWQPQOD/O3/PEdL2uGNazqOqMfzPK677jouu+wydu7cSRSpc9bLpQAiIiIiItOGa5m4FmQO68gF9XkeYZzgRwkD5YCBSkg1iNi4dxg/inEsk7xn858P7+WRHYMUMw6WAQlgmwYp22TfYI3//s0+LlzWQjE9/jb51a9+9dh/VyoVkiRp2Nc80yiAiIiIiMi0Z5oGrmngjs4xmd86+oFF9TkmtTBm074RdvfXsM36sMSDEeLgv1OuxcZ9I+zqr1JM547yq8hE0OR5EREREZnRLNMg4462CTYg4eirF3GSkPcs0u4r67glL00BREREREROCfmUzdnz89TCmCQ5NH/QoL4K4vsRy7oyzCm4Taxy5lMAEREREZFTgmebvOPVs1jelWWwHFAOIsKovj1roByyfHaWPzp3tlZAJpkCiIiIiIicMl6zqMDNf7iC9ae30jnatte1Tc5bXOCfrlrBJcvbml3ijKdD6CIiIiJySrliZTvrl7XwwAsDPLV7mJxn8/pV7SxoTR3/xUAYhgwMDFCtVie50plJAURERERETjkZ1+J3VrTzOyvaT/q1XV1dvOtd72LNmjWTUNnMpwAiIiIiInIS5s6dywc+8IFmlzFt6QyIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIiIiIiIg0jAKIyKgkSTBNE8/zml2KiIiISFO5rgvU748mmj3hn1FkmjJNkz179nDnnXeyaNEifN9vdkkiIiIiDee6Lo899hgDAwNYljXhn18BRGTU7NmzOeOMM9i2bRvd3d3EcdzskkREREQazjAMRkZGWL16NcViceI/fzIZ6yoi01Acx1QqFarVarNLEREREWkqwzBIp9OkUikMw5iwz5skiQKIiIiIiIg0RpIkOoQuIiIiIiKNowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINowAiIiIiIiINYydJ0uwaRERERETkFPH/AxDCbkM5Z59PAAAAAElFTkSuQmCC\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.625px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405px 61.3125px; transform-origin: 405px 61.3125px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eC = [0 1 1;0 0 1;1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eC^2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e1 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4375px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405px 10.2125px; text-wrap-mode: nowrap; transform-origin: 405px 10.2188px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 676.8px; font-family: 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\" alt=\"The matrices C and C^2 are shown in two different colors. A grid representing all the possible pairs of nodes has entries in each location showing the minimum number of steps, and the associated path, needed to travel between each pair. (The diagonal entries are blank, as these would represent traveling to the same node/island.) The four non-zero elements of C correspond to four pairs of islands that are connected with a single-step journey. The remaining two pairs (2 to 1 and 3 to 2) are connected with two-step journeys.\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAssumptions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e exists for the given connectivity matrix \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. This also means the problem is trivial for one or two islands, so your solution only needs to work when \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eC\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is at least 3-by-3.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = numbridges(C)\r\nn = sum(C==1,\"all\");\r\nend","test_suite":"%% Test case #1: m = 3\r\nC = zeros(3);\r\nidx = [3;4;8];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,2))\r\n\r\n%% Test case #2: m = 3\r\nC = zeros(3);\r\nidx = [3;4;7;8];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,2))\r\n\r\n%% Test case #3: m = 4\r\nC = zeros(4);\r\nidx = [2;7;8;10;13];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #4: m = 4\r\nC = zeros(4);\r\nidx = [4;5;7;8;9;14];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #5: m = 5\r\nC = zeros(5);\r\nidx = [5;8;11;12;14;18;22;23];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,3))\r\n\r\n%% Test case #6: m = 5\r\nC = zeros(5);\r\nidx = [2;5;8;14;20;21;24];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #7: m = 6\r\nC = zeros(6);\r\nidx = [3;4;7;11;18;23;24;26;30;32];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #8: m = 6\r\nC = zeros(6);\r\nidx = [3;11;12;13;18;19;28;32];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #9: m = 7\r\nC = zeros(7);\r\nidx = [3;4;6;8;13;14;20;21;23;31;36;47];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,6))\r\n\r\n%% Test case #10: m = 7\r\nC = zeros(7);\r\nidx = [4;11;13;14;19;22;23;26;28;30;34;35;37;38;45];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #11: m = 8\r\nC = zeros(8);\r\nidx = [2;4;5;6;8;12;13;17;27;39;44;48;54;58;60;62];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #12: m = 8\r\nC = zeros(8);\r\nidx = [3;9;12;20;24;29;30;31;33;44;48;50;53;54;58];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))\r\n\r\n%% Test case #13: m = 9\r\nC = zeros(9);\r\nidx = [8;9;10;14;15;22;25;26;29;33;36;42;44;47;48;50;53;54;55;67;80];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #14: m = 9\r\nC = zeros(9);\r\nidx = [8;10;22;32;37;40;43;45;47;53;56;57;62;64;69;70;73;77;79];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,7))\r\n\r\n%% Test case #15: m = 10\r\nC = zeros(10);\r\nidx = [2;5;8;13;16;20;24;27;28;36;43;49;53;62;71;75;77;83;86;87;95];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,5))\r\n\r\n%% Test case #16: m = 10\r\nC = zeros(10);\r\nidx = [4;9;14;21;22;35;37;38;44;47;50;51;53;55;59;61;63;66;69;76;77;84;85;86;90;97];\r\nC(idx) = 1;\r\nn = numbridges(C);\r\nassert(isequal(n,4))","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2025-11-10T02:22:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":87,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-04T20:34:09.000Z","updated_at":"2026-02-13T15:41:43.000Z","published_at":"2025-11-10T02:22:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe fabled city of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e consists of several islands connected by bridges. Due to tectonic forces, shoddy civil engineering, and the whims of the city's capricious dictator, Lord Ned, the number of islands and the arrangement of bridges are constantly changing.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, instead of a map, visitors to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are given a connectivity matrix (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e): a square matrix of 0s and 1s, where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 1 if there is a bridge from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 0 if not. Lord Ned’s diabolical nature means that the bridges can be one-way only. This means \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is not necessarily symmetric. That is, it is possible to have \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 1 (you can travel directly from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) = 0 (you can’t travel directly from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBecause Lord Ned likes to torture visitors with math problems, the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e transit system has an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-bridge pass which allows you to cross up to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bridges in one journey. Not surprisingly, the pricing scheme gets eye-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewateringly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e expensive as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e increases and once you purchase your pass, you are locked in to that price (at least until the connectivity matrix changes). Visitors are therefore advised to pick the smallest possible value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that allows them to get from any island to any other island in one journey.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo prepare for your upcoming trip to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedsburg\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to watch the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, you need to write a function that, given \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, returns the minimum \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e needed so that there is a path from every island to every other island in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps or fewer. That is, find the smallest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that every pair of islands is at most \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bridges away from each other.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA handy bit of math that might help is that the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"53\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e represents the number of paths of exactly \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps from island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to island \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. (Here, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"22\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the matrix power; that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"18\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"91\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, using matrix multiplication.) Therefore, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, then there is no way to get from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in exactly \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps. If \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, there is at least one path (of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps) from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Therefore, one way to solve the problem is to find the smallest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that, for every \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e pair, there is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esome\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ≤ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"21\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"79\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider this arrangement of three islands with four bridges (1→2, 1→3, 2→3, 3→1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"361\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"704\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are no bridges to get from 2 to 1 or from 3 to 2 in one step. However,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = [0 1 1;0 0 1;1 0 0];\\nC^2\\nans =\\n1 0 1\\n1 0 0\\n0 1 1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis means it is possible to get from 2 to 1 and from 3 to 2 in two steps. While there is no 2-step path from 3 to 1, this can be achieved in one step (C(3,1) = 1). So in this case, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e = 2: you can get from every island to every other island with a journey that crosses no more than two bridges.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"671\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"708\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"The matrices C and C^2 are shown in two different colors. A grid representing all the possible pairs of nodes has entries in each location showing the minimum number of steps, and the associated path, needed to travel between each pair. (The diagonal entries are blank, as these would represent traveling to the same node/island.) The four non-zero elements of C correspond to four pairs of islands that are connected with a single-step journey. The remaining two pairs (2 to 1 and 3 to 2) are connected with two-step journeys.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId7\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAssumptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhile it is dangerous to expect too much rational behavior from Lord Ned, he won't strand his citizens, so you can assume that a finite \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e exists for the given connectivity matrix \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This also means the problem is trivial for one or two islands, so your solution only needs to work when \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is at least 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connected components of a graph","description":"Adjacency matrix of an undirected graph is given. Return the number of connected components in the graph.","description_html":"\u003cp\u003eAdjacency matrix of an undirected graph is given. Return the number of connected components in the graph.\u003c/p\u003e","function_template":"function y = conctCom(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx=[0 1 0 0 0; 1 0 1 1 0; 0 1 0 1 1; 0 1 1 0 1; 0 0 1 1 0];\r\ny_correct = 1;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=[0 1 0 0 0; 1 0 0 0 0;0 0 0 1 0;0 0 1 0 1;0 0 0 1 0];\r\ny_correct = 2;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=[0 1 0 0 0 0 ; \r\n1 0 0 0 0 0; \r\n0 0 0 1 0 0; \r\n0 0 1 0 0 0 ; \r\n0 0 0 0 0 1; \r\n0 0 0 0 1 0];\r\ny_correct = 3;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n%%\r\nx=1;\r\ny_correct = 1;\r\nassert(isequal(conctCom(x),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":61,"test_suite_updated_at":"2014-06-18T16:25:21.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-18T07:37:21.000Z","updated_at":"2026-03-30T15:21:20.000Z","published_at":"2014-06-18T07:37:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAdjacency matrix of an undirected graph is given. Return the number of connected components in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":458,"title":"Parcel Routing","description":"Given a matrix that represent the distance along highways between major cities numbered 1 to _N_, provide the path and shortest distance from a given city, _from_, to a given city, _to_. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.","description_html":"\u003cp\u003eGiven a matrix that represent the distance along highways between major cities numbered 1 to \u003ci\u003eN\u003c/i\u003e, provide the path and shortest distance from a given city, \u003ci\u003efrom\u003c/i\u003e, to a given city, \u003ci\u003eto\u003c/i\u003e. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.\u003c/p\u003e","function_template":"function [route d] = parcel_route( from, to, graph )\r\n route = -1;\r\n d = -1;\r\nend","test_suite":"%%\r\n[route d] = parcel_route( 1, 5, zeros( 5 ) )\r\nassert(route == -1 \u0026\u0026 d == -1);\r\n\r\n%%\r\n[route d] = parcel_route( 1, 2, [0 0.320527862039621 0 0 0;0.320527862039621 0 0 0 0.85044688801616;0 0 0 0 0;0 0 0 0 0;0 0.85044688801616 0 0 0] );\r\nassert( isequal(route,[1 2]) \u0026\u0026 abs( d - 0.320528 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 2, [0 0 0 0 0.648056184801628;0 0 0.168504735306137 0 0;0 0.168504735306137 0 0 0;0 0 0 0 0;0.648056184801628 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 3, 3, [0 0 0 1.07077622171054 0.00497624606093106;0 0 0 0 0;0 0 0 0 0;1.07077622171054 0 0 0 0;0.00497624606093106 0 0 0 0] );\r\nassert( isequal(route,3) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 2, [0 0 0.478447257684744 0.52778921303553 0;0 0 0 0.344727452766697 0;0.478447257684744 0 0 0 0;0.52778921303553 0.344727452766697 0 0 0;0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 1, 4, [0 0 0 0 0;0 0 0 0 0;0 0 0 0 0;0 0 0 0 0;0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 10, 5, [0 0 0 0 0.758920911298127 1.17184862472796 0 0 0 0;0 0 0 0.229051389055984 0 0 0.110344033764499 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0.229051389055984 0 0 0 0 0 0 0 0;0.758920911298127 0 0 0 0 0 0.582786870390757 0 0 0.266149081187709;1.17184862472796 0 0 0 0 0 0.91437757836659 0 0 0.928664694998184;0 0.110344033764499 0 0 0.582786870390757 0.91437757836659 0 0.72845914907191 0.440667818657679 0.0752998054887686;0 0 0 0 0 0 0.72845914907191 0 0 0;0 0 0 0 0 0 0.440667818657679 0 0 0.72584117080215;0 0 0 0 0.266149081187709 0.928664694998184 0.0752998054887686 0 0.72584117080215 0] );\r\nassert( isequal(route,[10 5]) \u0026\u0026 abs( d - 0.266149 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 7, 3, [0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0.348270614748404 0 0.963402246386651 0 0 0 0;0 0 0.348270614748404 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.00647302663148808 0 0;0 0 0.963402246386651 0 0 0 0 1.11338837090812 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.00647302663148808 1.11338837090812 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 7, [0 0 0.529236850857286 0 0 0 1.60144982503606 0 0 0;0 0 0 0 0 0 0.828441215877115 0 0 0;0.529236850857286 0 0 0.0279102825979989 0 0 0 0 0 0.0544746812572747;0 0 0.0279102825979989 0 0 0 0 0 0 0;0 0 0 0 0 1.04094484718858 0 0 0 0;0 0 0 0 1.04094484718858 0 0 0 0 0.124040053577104;1.60144982503606 0.828441215877115 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.37043522751259;0 0 0.0544746812572747 0 0 0.124040053577104 0 0 0.37043522751259 0] );\r\nassert( isequal(route,[4 3 1 7]) \u0026\u0026 abs( d - 2.1586 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 4, 7, [0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.577543761888686 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.124487323633357 0 0 0.679813903514902;0 0.577543761888686 0 0 0 0 0.560623889702786 0 0 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0.124487323633357 0.560623889702786 0 0 0 0.250828758360099 0;0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.250828758360099 0 0 0.914651700028183;0 0 0 0.679813903514902 0 0 0 0 0.914651700028183 0] );\r\nassert( isequal(route,[4 7]) \u0026\u0026 abs( d - 0.124487 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 1, 9, [0 0 0.210714289971845 0 0 0.655795786759233 0 0 0.417975370686535 0.0762383289683841;0 0 0 0 0 0 0 0 0 0;0.210714289971845 0 0 0 0 0 0.627639413184948 0.546973506820504 0 0;0 0 0 0 0 0.44290978142888 0 0 0 0;0 0 0 0 0 0 0.494959375382896 0.199417369123429 0.61193318690704 0;0.655795786759233 0 0 0.44290978142888 0 0 0 0 0.295901565877421 0;0 0 0.627639413184948 0 0.494959375382896 0 0 0 0 0;0 0 0.546973506820504 0 0.199417369123429 0 0 0 0 0.882898432991531;0.417975370686535 0 0 0 0.61193318690704 0.295901565877421 0 0 0 0.0999710063468799;0.0762383289683841 0 0 0 0 0 0 0.882898432991531 0.0999710063468799 0] );\r\nassert( isequal(route,[1 10 9]) \u0026\u0026 abs( d - 0.176209 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 12, 3, [0 0.139438875035701 0.112367141305958 0 0 0 0 0 0 0.742115072015769 0 0 0.244537467584915 0 0;0.139438875035701 0 0.135942047224331 0 0 0 0 0 0 0 0.374140881779805 0 0.217860680093506 0.379818098539566 1.17229854239237;0.112367141305958 0.135942047224331 0 0 0 0 0 1.7792137360137 0.350752848520651 0 0 0.284985494377118 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.161446305533344 0 0 0 0.183948436622344 0 0 0;0 0 1.7792137360137 0 0 0 0.161446305533344 0 0 0 0 0 0 0 0;0 0 0.350752848520651 0 0 0 0 0 0 0 0 0 0 0 0.362973369969354;0.742115072015769 0 0 0 0 0 0 0 0 0 0.263865914379949 0 0 0 0;0 0.374140881779805 0 0 0 0 0 0 0 0.263865914379949 0 0 0 0 0;0 0 0.284985494377118 0 0 0 0.183948436622344 0 0 0 0 0 0 0 0;0.244537467584915 0.217860680093506 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.379818098539566 0 0 0 0 0 0 0 0 0 0 0 0 1.863621808387;0 1.17229854239237 0 0 0 0 0 0 0.362973369969354 0 0 0 0 1.863621808387 0] );\r\nassert( isequal(route,[12 3]) \u0026\u0026 abs( d - 0.284985 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 14, 13, [0 0 0 0.0850075668378245 0 0 0 0 0.0463952689981919 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.0850075668378245 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.831785345865625 0 0 0 0 0 0 0 0.300824537605104 0;0 0 0 0 0.831785345865625 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.316676635592728 0 0 0.18465657297998 0 0;0.0463952689981919 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.316676635592728 0 0 0 0 0 0 2.01596808102817;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0.735349103904233 0 0;0 0 0 0 0 0 0 0.18465657297998 0 0 0 0.735349103904233 0 0 0;0 0 0 0 0.300824537605104 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 2.01596808102817 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 8, 8, [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.669844066951192 0 0 1.79425332134151 0 0 0 0;0 0 0 0 0 0.354813543185228 0.350829365088585 0.334017411457367 0 0 0.750194269879854 0 0 0 0.837083783283494;0 0 0 0 0 0 0 0 0 0 0 0.7666462425288 0 0 0;0 0 0 0 0 0 0 0 0 0 0.335432927184154 0.290662441159473 0 0 0;0 0 0.354813543185228 0 0 0 0 0 0 0 0 0.612746104915618 0 1.3702409817804 0;0 0 0.350829365088585 0 0 0 0 0 0 0 0 0 0 0 0;0 0.669844066951192 0.334017411457367 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 1.79425332134151 0.750194269879854 0 0.335432927184154 0 0 0 0 0 0 0 0 0 0;0 0 0 0.7666462425288 0.290662441159473 0.612746104915618 0 0 0 0 0 0 0.600235691901178 0 0;0 0 0 0 0 0 0 0 0 0 0 0.600235691901178 0 0 0;0 0 0 0 0 1.3702409817804 0 0 0 0 0 0 0 0 0.25725306655894;0 0 0.837083783283494 0 0 0 0 0 0 0 0 0 0 0.25725306655894 0] );\r\nassert( isequal(route,8) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 9, 2, [0 0 0 0 0 0 0 0.216161539093326 0 0 0 0 0 0 0;0 0 0 0.154899548332433 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0.195632123891572 0 0.638112022611646 0 0;0 0.154899548332433 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.924920233869358 0 0 0 0 0 0 0.225753938901222;0 0 0 0 0 0 0 0 0 0 0 0.105130198814148 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.216161539093326 0 0 0 0.924920233869358 0 0 0 0.283480661544537 0 0 0 0 0 0;0 0 0 0 0 0 0 0.283480661544537 0 0 0.860820315822094 0 0 0.114189406386242 0;0 0 0 0 0 0 0 0 0 0 0.777006911310097 0.0395282910845656 0.559642782958394 0.0374763085984708 0;0 0 0.195632123891572 0 0 0 0 0 0.860820315822094 0.777006911310097 0 0.107327989339846 0 0 0;0 0 0 0 0 0.105130198814148 0 0 0 0.0395282910845656 0.107327989339846 0 0 0 0;0 0 0.638112022611646 0 0 0 0 0 0 0.559642782958394 0 0 0 0 0;0 0 0 0 0 0 0 0 0.114189406386242 0.0374763085984708 0 0 0 0 0;0 0 0 0 0.225753938901222 0 0 0 0 0 0 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 6, 8, [0 1.00600776349789 0 0 0.409642943366229 0 0 0 0 0 0 0 0.780942905018081 0.218269812307052 0;1.00600776349789 0 0 0 0 0 0 0 0 0.604439587022491 0 0 0 0 0;0 0 0 2.19497071462911 0 0.384068674620751 0 0 0 0.752596352506117 0.210553220187945 0 0 0 0.101200876472261;0 0 2.19497071462911 0 0 0 0 0 0 0 0 0.0821684991109088 0 0 1.39540244685607;0.409642943366229 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.384068674620751 0 0 0 0.0278385656290563 0 0 0 0 0 0 0 0;0 0 0 0 0 0.0278385656290563 0 0 0 0 0.314664537582249 0 0 0 0.157551652892199;0 0 0 0 0 0 0 0 0 0 1.37753279184511 0 0 0.647734508061038 0.538120114299927;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.604439587022491 0.752596352506117 0 0 0 0 0 0 0 0.38099752988379 0 0 0 0;0 0 0.210553220187945 0 0 0 0.314664537582249 1.37753279184511 0 0.38099752988379 0 0 0 0 0;0 0 0 0.0821684991109088 0 0 0 0 0 0 0 0 0.724941186609613 0 0;0.780942905018081 0 0 0 0 0 0 0 0 0 0 0.724941186609613 0 0 0;0.218269812307052 0 0 0 0 0 0 0.647734508061038 0 0 0 0 0 0 0;0 0 0.101200876472261 1.39540244685607 0 0 0.157551652892199 0.538120114299927 0 0 0 0 0 0 0] );\r\nassert( isequal(route,[6 7 15 8]) \u0026\u0026 abs( d - 0.72351 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 15, 1, [0 0 0 0 0 0 0 0 0 0 0 0.444956179153313 0.694837045312089 0 0 0 1.21296662658388 0 1.56620351515086 0.139996151546743;0 0 0.436509042497808 0 0 0 0 0 0 0.51021617110356 0.382775864014207 0 0 0 0 0 0 0 0.0458660640982067 0;0 0.436509042497808 0 0.142843784706697 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.142843784706697 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.923358421898822 0 0 0 0 0 0 0.535622573665002 0 0 0.0623807988546017 0 0 0 0;0 0 0 0 0.923358421898822 0 0 0 0 0 0 0 0 0 0.0225268450194125 0.789248499651178 0.131644262096824 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0.157622272676696 0.474149476188578 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 1.38990078830045 0 0 0 0 0.691403775437505 0;0 0.51021617110356 0 0 0 0 0 0 0 0 0.597507997139564 0 0 0 0.354205526419423 0 0 0 0 0;0 0.382775864014207 0 0 0 0 0 0 0 0.597507997139564 0 0 0 0.659672915756231 0 0 0 0 0 0;0.444956179153313 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.138835508200668;0.694837045312089 0 0 0 0.535622573665002 0 0.157622272676696 0 0 0 0 0 0 0 0 0.112112626230952 0 0 0 0.0843937952650982;0 0 0 0 0 0 0.474149476188578 0 1.38990078830045 0 0.659672915756231 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.0225268450194125 0 0 0 0.354205526419423 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.0623807988546017 0.789248499651178 0 0 0 0 0 0 0.112112626230952 0 0 0 0 0 0 2.40412068693751;1.21296662658388 0 0 0 0 0.131644262096824 0 0 0 0 0 0 0 0 0 0 0 0 0.332961917093088 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.464569385286 0;1.56620351515086 0.0458660640982067 0 0 0 0 0 0 0.691403775437505 0 0 0 0 0 0 0 0.332961917093088 1.464569385286 0 0;0.139996151546743 0 0 0 0 0 0 0 0 0 0 0.138835508200668 0.0843937952650982 0 0 2.40412068693751 0 0 0 0] );\r\nassert( isequal(route,[15 6 16 13 20 1]) \u0026\u0026 abs( d - 1.14828 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 6, 9, [0 0.366176160541789 0.786653302253499 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.21056171145861;0.366176160541789 0 0 0 0 0 0 0 0 1.00794652016003 0 0 0 0 0 0 0 0 0 0;0.786653302253499 0 0 0 0.00852440175782365 0 0 0 0 0 0.17749671083585 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.104971160045632 0 0.612122585766863 0 0.283798036821908;0 0 0.00852440175782365 0 0 0 0.643083445674635 0 0 0 1.48191061231689 0 0 0 0.200776975353452 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.20656027667801 0 0 0;0 0 0 0 0.643083445674635 0 0 0 0.0293301078099069 0 0 0.0684877514911584 0.244866042619905 0 0 0 0 0.844967783108164 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.0293301078099069 0 0 0 0 0.112122931478046 0 0 0 0.904924565740344 0 0 0 0;0 1.00794652016003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.17749671083585 0 1.48191061231689 0 0 0 0 0 0 0.356911349006201 0 0 0.157837394902406 0 0 0 0 0;0 0 0 0 0 0 0.0684877514911584 0 0.112122931478046 0 0.356911349006201 0 0.682956498987976 0.369934947078526 0 0 0 0 0 0;0 0 0 0 0 0 0.244866042619905 0 0 0 0 0.682956498987976 0 0 0 0 1.43719870645473 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.369934947078526 0 0 0 0 0 0 0 0;0 0 0 0 0.200776975353452 0 0 0 0 0 0.157837394902406 0 0 0 0 0.0962643536575907 0 0 0 0;0 0 0 0.104971160045632 0 0 0 0 0.904924565740344 0 0 0 0 0 0.0962643536575907 0 0 0 0 0.884861315533158;0 0 0 0 0 1.20656027667801 0 0 0 0 0 0 1.43719870645473 0 0 0 0 0.00316254045496533 0.917601066178612 0;0 0 0 0.612122585766863 0 0 0.844967783108164 0 0 0 0 0 0 0 0 0 0.00316254045496533 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.917601066178612 0 0 0;0.21056171145861 0 0 0.283798036821908 0 0 0 0 0 0 0 0 0 0 0 0.884861315533158 0 0 0 0] );\r\nassert( isequal(route,[6 17 18 7 9]) \u0026\u0026 abs( d - 2.08402 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 15, 8, [0 0 0 0 0 0 0 0.444927230771171 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0.0904807891398611 0 0 0.117980802815763 0 0 0 0 0 0 0 0 0 0 0.786791961925432 0 0;0 0 0 0 0 0 0 0 0 0.159132007464481 0.110433064086588 0 0 0 0 0 0 0 0 0.139982926657546;0 0.0904807891398611 0 0 0.388870980511861 0 0 0 0 0 0 0.973527639623757 0 0 0 0 0 0 0 0;0 0 0 0.388870980511861 0 0 0 0.305597627987039 0 0 0 0 0.027077532916115 0 0 0 0 0 0.290796760442986 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.117980802815763 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.27266472620458 0.665594866955372 0 0.626568354787985;0.444927230771171 0 0 0 0.305597627987039 0 0 0 0 0 0 0 0 0 0 0 0 0 0.498229667608556 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.159132007464481 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.110433064086588 0 0 0 0 0 0 0 0 0 0.611004915345871 0 0 0 0.561899811991367 0 0 0;0 0 0 0.973527639623757 0 0 0 0 0 0 0 0 0.949329689605504 0 0 0 0 0 0 0;0 0 0 0 0.027077532916115 0 0 0 0 0 0.611004915345871 0.949329689605504 0 0 0 0 0 0.45822991145669 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.916926430883478 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0944227233455792;0 0 0 0 0 0 1.27266472620458 0 0 0 0.561899811991367 0 0 0 0 0 0 0.0123263072768274 0 0;0 0.786791961925432 0 0 0 0 0.665594866955372 0 0 0 0 0 0.45822991145669 0 0 0 0.0123263072768274 0 0.708053638455894 0;0 0 0 0 0.290796760442986 0 0 0.498229667608556 0 0 0 0 0 0.916926430883478 0 0 0 0.708053638455894 0 0;0 0 0.139982926657546 0 0 0 0.626568354787985 0 0 0 0 0 0 0 0 0.0944227233455792 0 0 0 0] );\r\nassert( isequal(route,-1) \u0026\u0026 abs( d - -1 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 4, [0 0 0 0 0 0 0 0 0 0.482056160228392 0 0 0 0 0 0 0 0 0.00508309589200806 0;0 0 0.342764171753101 0 0.592230924022738 0 0 0 0 0 0 0 0 0 0 0 0.616196530219501 0 0.105964156030294 0;0 0.342764171753101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.06756913797405 0 0 0 0 0;0 0.592230924022738 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 1.75169005582658 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.198256254136309 0 0.117040404671406 0.742253008190119 0 0 0 0 0 0 0 0 0;0 0 0 0 0 1.75169005582658 0.198256254136309 0 0 0.193487168668438 0 0 0 0 0 0.213470445629309 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.672768253513765 0 0 0 0 0 0 0 0;0.482056160228392 0 0 0 0 0 0.117040404671406 0.193487168668438 0 0 0.182397544709499 0 0 0 0 0 0 0 0.352313653363684 0;0 0 0 0 0 0 0.742253008190119 0 0 0.182397544709499 0 0.863897537114491 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0.672768253513765 0 0.863897537114491 0 0 0 0 0 0.849422540372472 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.126177070732391 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 1.06756913797405 0 0 0 0 0 0 0 0 0.126177070732391 0 0 0 0 0.810675752838635 0.192588746332897 0;0 0 0 0 0 0 0 0.213470445629309 0 0 0 0 0 0 0 0 0 0 0 0;0 0.616196530219501 0 0 0 0 0 0 0 0 0 0.849422540372472 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.810675752838635 0 0 0 0 0;0.00508309589200806 0.105964156030294 0 0 0 0 0 0 0 0.352313653363684 0 0 0 0 0.192588746332897 0 0 0 0 0.473102743220109;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.473102743220109 0] );\r\nassert( isequal(route,[5 2 19 15 4]) \u0026\u0026 abs( d - 1.95835 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 18, 3, [0 0 0 0 0 0 0.588131422298983 0 0 0 0 0 0 0 0 0 0 0 0.411615488806083 0;0 0 0 0 0 0 0 0 0 0.148093137302263 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.513126690185934 0.314081294727961 0 0 0 0 0 0 0 0 0 0 0.105622237791164 0 0;0 0 0 0 0.0233388222703319 0 0 0.620281238898666 0 0 0 1.01777898612281 0 0 1.02802169259185 0 0 0 0 0.829878923718159;0 0 0 0.0233388222703319 0 0.103664033766553 0 0 0 0 0.800687507355036 0.724909646173659 0 0 0 0 0 0 0 0;0 0 0.513126690185934 0 0.103664033766553 0 0 0 0 0 0 0 0.51932792913499 0.111508583765534 0 0 0 0 0 0;0.588131422298983 0 0.314081294727961 0 0 0 0 0 0.632615202648636 0 0 0 1.12039468709595 0 0 0 0 0 0 0;0 0 0 0.620281238898666 0 0 0 0 0.665853755155897 0 0.443519419164187 0 0.0287540443670959 0 0 0 0 0 0 0;0 0 0 0 0 0 0.632615202648636 0.665853755155897 0 0 0 0 0 0 0 0 0 0 0 0.341087071649035;0 0.148093137302263 0 0 0 0 0 0 0 0 0.0523829918450132 0 0 0 0 0 0 0.401940201122634 0.691832558529399 0;0 0 0 0 0.800687507355036 0 0 0.443519419164187 0 0.0523829918450132 0 0.491690939364275 0 0.757845451055127 0 0 0 0.024463668194654 0 0;0 0 0 1.01777898612281 0.724909646173659 0 0 0 0 0 0.491690939364275 0 0 0 0 0 0 0 0 1.02836268723464;0 0 0 0 0 0.51932792913499 1.12039468709595 0.0287540443670959 0 0 0 0 0 0 0 0 0 0 0 0.366143225287491;0 0 0 0 0 0.111508583765534 0 0 0 0 0.757845451055127 0 0 0 0.223088374978438 0 0 0 0 0;0 0 0 1.02802169259185 0 0 0 0 0 0 0 0 0 0.223088374978438 0 0.344909749483892 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.344909749483892 0 0 0 0.353158597614942 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0.105622237791164 0 0 0 0 0 0 0.401940201122634 0.024463668194654 0 0 0 0 0 0 0 0 0;0.411615488806083 0 0 0 0 0 0 0 0 0.691832558529399 0 0 0 0 0 0.353158597614942 0 0 0 0.849677928981906;0 0 0 0.829878923718159 0 0 0 0 0.341087071649035 0 0 1.02836268723464 0.366143225287491 0 0 0 0 0 0.849677928981906 0] );\r\nassert( isequal(route,[18 3]) \u0026\u0026 abs( d - 0.105622 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 17, 23, [0 0.151141399637051 0 0 0 0 0 0 0 0 0 0.105216194021975 0 0 0 0 0 0.437381445735918 0 0 0.949941070771521 0 0 0 0;0.151141399637051 0 0 0 0 0 0 0 1.22583368379244 0 0 0.079829221583307 0.71041636270324 0 0 0 0.1075924794072 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 1.21948687450578 0 0.567263036089771 0 0 0 0 0 0.857795458880245 0 0 0 0 0.731569267553795 0 0 0;0 0 0 0 0 0 0.186039341309778 0 0 0 0 0 0 0 0 0 0 2.00348310018009 0 0 0 0 0 0 0;0 0 0 0 0 1.32070145417138 0 0 0 0 0 0 0 0 0 0 0 0.430765092398605 0 0 0 0 0 0.46856479561555 0;0 0 0 0 1.32070145417138 0 0 0.203767017753022 0 0 0 0 0 0 0.487213897131877 0 0 0.896335225888555 0 0 0 0 0 0 0;0 0 0 0.186039341309778 0 0 0 0 0 0 0 0 0.406945507381253 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.203767017753022 0 0 0.15769661193131 0 0.65001597680104 0 0 0 0 0 0 0.15666137867965 0 0 0 0 0.723509833846064 0 0;0 1.22583368379244 1.21948687450578 0 0 0 0 0.15769661193131 0 0 0 0 0 0 0 0 0.508542446617735 0 0 0.696934855529271 0.169312519482881 0 0.00704092733099992 0 0;0 0 0 0 0 0 0 0 0 0 0 0 1.08493079525094 0.214254564261975 0 0.425013648805044 0 0 0 0 0 0 0 0 0.00543872970590864;0 0 0.567263036089771 0 0 0 0 0.65001597680104 0 0 0 0 0 0 0 0 0.696979111426999 0.525282567629852 0 0.621146400617813 1.20050590589561 0 0 0 0;0.105216194021975 0.079829221583307 0 0 0 0 0 0 0 0 0 0 0.459862031186997 0 0 0 0 0 0 0.698687249438191 0 0 0.00200957746053532 0 0;0 0.71041636270324 0 0 0 0 0.406945507381253 0 0 1.08493079525094 0 0.459862031186997 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.214254564261975 0 0 0 0 0.380308744719566 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0.487213897131877 0 0 0 0 0 0 0 0.380308744719566 0 0 0.0449909078512449 0 0 1.22341039971646 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.425013648805044 0 0 0 0 0 0 0 0 0 0 0 0 1.43760755773283 0.719177032769178 0;0 0.1075924794072 0.857795458880245 0 0 0 0 0 0.508542446617735 0 0.696979111426999 0 0 0 0.0449909078512449 0 0 0 0 0 0 0 0 0 0;0.437381445735918 0 0 2.00348310018009 0.430765092398605 0.896335225888555 0 0.15666137867965 0 0 0.525282567629852 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0.696934855529271 0 0.621146400617813 0.698687249438191 0 0 1.22341039971646 0 0 0 0 0 1.1519343197436 0 0 0 0;0.949941070771521 0 0 0 0 0 0 0 0.169312519482881 0 1.20050590589561 0 0 0 0 0 0 0 0 1.1519343197436 0 0 0 0 0.214330337662627;0 0 0.731569267553795 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0.723509833846064 0.00704092733099992 0 0 0.00200957746053532 0 0 0 1.43760755773283 0 0 0 0 0 0 0 0 0;0 0 0 0 0.46856479561555 0 0 0 0 0 0 0 0 0 0 0.719177032769178 0 0 0 0 0 0 0 0 0.649429697531317;0 0 0 0 0 0 0 0 0 0.00543872970590864 0 0 0 0 0 0 0 0 0 0 0.214330337662627 0 0 0.649429697531317 0] );\r\nassert( isequal(route,[17 2 12 23]) \u0026\u0026 abs( d - 0.189431 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 2, 9, [0 0 0 0 0 0.390568942736463 0.253317405921882 0 0 0 0 0 0 0 0 0.035303866587423 0 0.0932427029924401 0 0.228317786309953 0 0 0 0 0;0 0 0 0 0 0 0.22325539367323 0.0737968434096563 0 0.0216391156829114 1.01817561468837 0 0 0.166613690540579 0 0 0 0 0 0.0929136548216463 0 0 0 0 0;0 0 0 0 0 0.324975382720294 0 0 0 0 0 0 0 0 0.257683850031889 0 0 0 0.818836795828793 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0.0227807501033899 0 0 0 0 0.254392094407223 0 0 0 0 0 0.499630884751228 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.645309316664204 0 0 0 0 0 0.377002225744615 0 0 0 0 0;0.390568942736463 0 0.324975382720294 0 0 0 0 0 0 0 0 0.381625987614713 0.187530187877611 0 0 0 0 0 1.03662835165178 0 0 0 0 0 0;0.253317405921882 0.22325539367323 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.420116352005782 0.288516971344659 0 0 0 0 0.290423766876168 0;0 0.0737968434096563 0 0 0 0 0 0 0 0 0 0 0 1.14302504189143 0 0.894497023541543 0 0 0 0 0 0 0.181212342324972 0 0.4790219658659;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.66947645498941 0 0 0 0 0 0 0 0.881432911415172 0.190738003819626 0;0 0.0216391156829114 0 0 0 0 0 0 0 0 0 0 0 0 0 0.406383564162139 0 0 0 0 0 0 0 0 0.0727730905533963;0 1.01817561468837 0 0 0 0 0 0 0 0 0 0.968244418446258 0 0 0.528273242216383 0.125653272829919 0 0 0 0 0 0 0 0 0;0 0 0 0.0227807501033899 0 0.381625987614713 0 0 0 0 0.968244418446258 0 0 0 0 0.545221500471632 0 0 0.602095719309548 0 0 0 0 0 0;0 0 0 0 0 0.187530187877611 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.926256705235548 0 0 0 0;0 0.166613690540579 0 0 0.645309316664204 0 0 1.14302504189143 0 0 0 0 0 0 0 0 0 0.21560951711239 0 0 0 0 0 0 0;0 0 0.257683850031889 0 0 0 0 0 0.66947645498941 0 0.528273242216383 0 0 0 0 0 0 0 0 0 0.213055228450905 0 0 0 0;0.035303866587423 0 0 0 0 0 0 0.894497023541543 0 0.406383564162139 0.125653272829919 0.545221500471632 0 0 0 0 0.450996873658263 0 0 0 0 0 0 0 0;0 0 0 0.254392094407223 0 0 0 0 0 0 0 0 0 0 0 0.450996873658263 0 0 0 0 0 0 0.219529444302005 0 0;0.0932427029924401 0 0 0 0 0 0.420116352005782 0 0 0 0 0 0 0.21560951711239 0 0 0 0 0 0 0 0.863470148104069 0 0.444628451921207 0;0 0 0.818836795828793 0 0 1.03662835165178 0.288516971344659 0 0 0 0 0.602095719309548 0 0 0 0 0 0 0 0 0 0.109259232718513 0 0 0;0.228317786309953 0.0929136548216463 0 0 0.377002225744615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.615496286883062;0 0 0 0 0 0 0 0 0 0 0 0 0.926256705235548 0 0.213055228450905 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.863470148104069 0.109259232718513 0 0 0 0 0.0137884729469751 0;0 0 0 0.499630884751228 0 0 0 0.181212342324972 0.881432911415172 0 0 0 0 0 0 0 0.219529444302005 0 0 0 0 0 0 0.365687691203556 0;0 0 0 0 0 0 0.290423766876168 0 0.190738003819626 0 0 0 0 0 0 0 0 0.444628451921207 0 0 0 0.0137884729469751 0.365687691203556 0 0;0 0 0 0 0 0 0 0.4790219658659 0 0.0727730905533963 0 0 0 0 0 0 0 0 0 0.615496286883062 0 0 0 0 0] );\r\nassert( isequal(route,[2 7 24 9]) \u0026\u0026 abs( d - 0.704417 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 12, 4, [0 0 0 0 0 0.714172260222902 0 0 0 0 0 0 0 0 0 0 0 0 0 0.361655390928043 0 0 0 0 0;0 0 0 0 0 0.181492418867339 0 0 0 0 0 0 0 0 0 0 0 0 0.22307965545124 0 0 0 0 0 0.779096496125678;0 0 0 0 0 0 0.47292346615082 0 0 0.168841046075892 0 0 0 0 0 0 0 0 0 0.0217708463553388 0 0 0.667747131809592 0 0;0 0 0 0 0 0 0 0.57532352865356 0 0 1.14531063150095 0 0 0 0 0 0 0 0 1.81120439254402 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0.347520308679668 0 0 0 0 1.19301977580358 0 0.820270346367214 0 0 0.0596854204267023 0 0.365147722552969 0.160828167983497 0;0.714172260222902 0.181492418867339 0 0 0 0 0.993473525288174 0 0 0 0 0 0 0 0 0 0 0 0 0.900267645686867 0 0 0 0 0;0 0 0.47292346615082 0 0 0.993473525288174 0 0 0 0 0.303524976604928 0 0 0.988108387042638 0 0 0 0 1.09908596860658 0 0 0 0 0 0;0 0 0 0.57532352865356 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0496932258637628 0 0 0 0 0 0 0.220051533000778 0 0 0;0 0 0.168841046075892 0 0 0 0 0 0 0 0 0 0.415893111213311 0 0 0 0 0 0 0 0 0 0 0.252874847836154 0.7525160069564;0 0 0 1.14531063150095 0.347520308679668 0 0.303524976604928 0 0 0 0 0 0.315427799185682 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.0659140954680451 0.229430180128888 0 0 0 1.02421541676855 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0.415893111213311 0.315427799185682 0 0 0 1.43148800286315 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0.988108387042638 0 0 0 0 0.0659140954680451 0 0 0 0 0.0723048054691602 0.570598773372364 0 0 0 0 0 0 0.816798020448907;0 0 0 0 0 0 0 0 0.0496932258637628 0 0 0.229430180128888 1.43148800286315 0 0 0.0969052461008522 0 0 0.562394406606289 0 0 0 0 0 0;0 0 0 0 1.19301977580358 0 0 0 0 0 0 0 0 0 0.0969052461008522 0 0.830027198843182 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.0723048054691602 0 0.830027198843182 0 0 0 0 0.471570888980097 0 0 0 0;0 0 0 0 0.820270346367214 0 0 0 0 0 0 0 0 0.570598773372364 0 0 0 0 0 0 0 0 0 0 0;0 0.22307965545124 0 0 0 0 1.09908596860658 0 0 0 0 1.02421541676855 0 0 0.562394406606289 0 0 0 0 0 0 0 0 0 0;0.361655390928043 0 0.0217708463553388 1.81120439254402 0 0.900267645686867 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0.0596854204267023 0 0 0 0 0 0 0 0 0 0 0 0.471570888980097 0 0 0 0 0.920738174557066 0 0 0;0 0 0 0 0 0 0 0 0.220051533000778 0 0 0 0 0 0 0 0 0 0 0 0.920738174557066 0 0 0 0;0 0 0.667747131809592 0 0.365147722552969 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.431353900603143;0 0 0 0 0.160828167983497 0 0 0 0 0.252874847836154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.139899289183316;0 0.779096496125678 0 0 0 0 0 0 0 0.7525160069564 0 0 0 0.816798020448907 0 0 0 0 0 0 0 0 0.431353900603143 0.139899289183316 0] );\r\nassert( isequal(route,[12 14 17 21 5 11 4]) \u0026\u0026 abs( d - 2.16231 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 21, 9, [0 1.30397831279197 0 0 0 0 0 0 0.205205702932437 0 0 0 0 0.462991342326146 0 0.0919395070383298 0 0 0 0 0 0 0 0.788285106392582 0;1.30397831279197 0 0 0 0 1.05960547137835 0 0 0.153644616706166 0 0 0.860262579703983 0 0 0 0 0 0 0 0 0 0 0 0.773468224481749 0;0 0 0 0.13195985000036 0 0 0 1.51813223209895 0 0 0 0 0 0.0156327604904785 0 1.27556519583119 0.93793259222666 0 0 0 0 0 0 0 0;0 0 0.13195985000036 0 0 0 0.458734989962526 0 0 0 0 0 0 0 0 0 0 0 0.835183344604242 0.11324698880843 0 0 1.27194671889278 0 0.873215204449672;0 0 0 0 0 0 0.0522387394084536 0.441514133149768 0 0 0 0 0 0 0 0 0 0 0 0.104845115642955 0 0 0 0 0;0 1.05960547137835 0 0 0 0 0 0 0 0 0 0.323427832607934 0 0 0 0 0 0.548178891330981 0 0 0 1.78927187214965 0 0 0;0 0 0 0.458734989962526 0.0522387394084536 0 0 0.128458273651367 0 1.10447307401052 0 0 1.60635812839544 0.490059715639469 0 0 0 0 0.87297695015148 0 0 0 0.0385154612576837 0 0;0 0 1.51813223209895 0 0.441514133149768 0 0.128458273651367 0 0 0.0426997868037813 0 0 0 0.316089962309322 0.556234063736422 0 0 0 0 0 0 0 0.125426946797567 0 0.501620852747415;0.205205702932437 0.153644616706166 0 0 0 0 0 0 0 0 0 0 0 0 0 1.22841179064666 0 0.506177174936367 0 0 0 0.139674374757514 0 0 0;0 0 0 0 0 0 1.10447307401052 0.0426997868037813 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.695460494256037;0 0 0 0 0 0 0 0 0 0 0 1.09170738934842 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0.860262579703983 0 0 0 0.323427832607934 0 0 0 0 1.09170738934842 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 1.60635812839544 0 0 0 0 0 0 0 0 0 0 0 0 1.27248881503698 0 0 0 0 0.162219187624835;0.462991342326146 0 0.0156327604904785 0 0 0 0.490059715639469 0.316089962309322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.111140488918591 0.0639936929497993;0 0 0 0 0 0 0 0.556234063736422 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0.0919395070383298 0 1.27556519583119 0 0 0 0 0 1.22841179064666 0 0 0 0 0 0 0 0.772788879713252 0 0 0 0.00743946114818939 0 0.662296573850469 0 0;0 0 0.93793259222666 0 0 0 0 0 0 0 0 0 0 0 0 0.772788879713252 0 0 0 0 0 0 0 0 0.235465388137087;0 0 0 0 0 0.548178891330981 0 0 0.506177174936367 0 0 0 0 0 0 0 0 0 0.596123003045261 0 0 0.50807778971881 0 0.192149930306311 0;0 0 0 0.835183344604242 0 0 0.87297695015148 0 0 0 0 0 0 0 0 0 0 0.596123003045261 0 1.75354812715354 0 0 0 0.230875543406997 0.402865723829543;0 0 0 0.11324698880843 0.104845115642955 0 0 0 0 0 0 0 1.27248881503698 0 0 0 0 0 1.75354812715354 0 0 0 0.286957051522517 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00743946114818939 0 0 0 0 0 0 0 0 0.12234328650336;0 0 0 0 0 1.78927187214965 0 0 0.139674374757514 0 0 0 0 0 0 0 0 0.50807778971881 0 0 0 0 0 0 0.0229366876888033;0 0 0 1.27194671889278 0 0 0.0385154612576837 0.125426946797567 0 0 0 0 0 0 0 0.662296573850469 0 0 0 0.286957051522517 0 0 0 0 0;0.788285106392582 0.773468224481749 0 0 0 0 0 0 0 0 0 0 0 0.111140488918591 0 0 0 0.192149930306311 0.230875543406997 0 0 0 0 0 0;0 0 0 0.873215204449672 0 0 0 0.501620852747415 0 0.695460494256037 0 0 0.162219187624835 0.0639936929497993 0 0 0.235465388137087 0 0.402865723829543 0 0.12234328650336 0.0229366876888033 0 0 0] );\r\nassert( isequal(route,[21 25 22 9]) \u0026\u0026 abs( d - 0.284954 ) \u003c 1e-4 );\r\n\r\n%%\r\n[route d] = parcel_route( 5, 5, [0 0 0.235687387990488 0 0 0.518662908555243 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00169033754843562 1.18573193396702 0 0 0 0 0;0.235687387990488 0 0 0 0.350144748614205 0 0 0.499051956190166 0 0 0 0 0 0 0 0.428154355783706 0 0 0 0.988646147648288 0 0 0 0 0.766292681932783;0 0 0 0 0 0 0 0 0 0.306976149669423 0 0 0 0 0 0 0.148608071246878 0 0 0 0 0 0 0 0;0 0 0.350144748614205 0 0 0 0.366820040758671 0 0 0 0.947779130029861 0 0 0 0 0 0 0 0 0.781367996905263 0 0 0 0 0;0.518662908555243 0 0 0 0 0 0.28467286265608 0 0 0 0 0 0 0 0 0 0 0 0.814169013559602 0 0 0 0 0 0.514510683872373;0 0 0 0 0.366820040758671 0.28467286265608 0 0 0 0 0 0.137928171247269 0 0 0 0 0 0.581896713318172 0 0 0 1.00288388568789 0.926366539848811 0 0;0 0 0.499051956190166 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.211128675902371 0 0 0 0 0 0 0 0 0;0 0 0 0.306976149669423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.332300868928405;0 0 0 0 0.947779130029861 0 0 0 0 0 0 0 0 0 0.140274584917356 0 0 0 0.276337784565307 0 0 0 0 0 0;0 0 0 0 0 0 0.137928171247269 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.422423933152413 0 0 0 0 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.16443124331918 0 0 0 0.113521569230241 0 0 0.431552467605628 0;0 0 0 0 0 0 0 0 0 0 0.140274584917356 0 0 0 0 0 0 1.01590071824433 0 0 0 0 0 0 0;0 0 0.428154355783706 0 0 0 0 0 0.211128675902371 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 0.148608071246878 0 0 0 0 0 0 0 0 0 0.16443124331918 0 0 0 0 0 0.816401527141028 0 0 0 0 1.51727966787778;0 0 0 0 0 0 0.581896713318172 0 0 0 0 0 0 0 1.01590071824433 0 0 0 0 0 0.0315916186043663 0 0 0 0;0 0.00169033754843562 0 0 0 0.814169013559602 0 0 0 0 0.276337784565307 0 0.422423933152413 0 0 0 0 0 0 0 0 0 0 0.113122720118215 0;0 1.18573193396702 0.988646147648288 0 0.781367996905263 0 0 0 0 0 0 0 0 0 0 0 0.816401527141028 0 0 0 0 0 0 0 1.22595526329414;0 0 0 0 0 0 0 0 0 0 0 0 0 0.113521569230241 0 0 0 0.0315916186043663 0 0 0 0 0 0.0278718835255773 0;0 0 0 0 0 0 1.00288388568789 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.627233109842477 0 0;0 0 0 0 0 0 0.926366539848811 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.627233109842477 0 0.210579136296008 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0.431552467605628 0 0 0 0 0.113122720118215 0 0.0278718835255773 0 0.210579136296008 0 0;0 0 0.766292681932783 0 0 0.514510683872373 0 0 0 0.332300868928405 0 0 0 0 0 0 1.51727966787778 0 0 1.22595526329414 0 0 0 0 0] );\r\nassert( isequal(route,5) \u0026\u0026 abs( d - 0 ) \u003c 1e-4 );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":26,"test_suite_updated_at":"2012-03-07T20:02:13.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-06T18:48:13.000Z","updated_at":"2026-03-30T17:22:55.000Z","published_at":"2012-03-07T20:03:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix that represent the distance along highways between major cities numbered 1 to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, provide the path and shortest distance from a given city,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrom\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, to a given city,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Assume that 0 represents no direct path between two cities. If there is no solution to the problem, return -1 for both the path and the distance.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1169,"title":"Count the Number of Directed Cycles in a Graph","description":"Given an asymmetric adjacency matrix, determine the number of unique directed cycles.\r\nFor example, the graph represented by adjacency matrix\r\nA = [\r\n 0 1 1 0;\r\n 1 1 0 1;\r\n 1 0 0 1;\r\n 1 1 0 0];\r\nhas 7 cycles. They are:\r\n[2 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 1]\r\n[2 -\u003e 4 -\u003e 2]\r\n[1 -\u003e 2 -\u003e 4 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 4 -\u003e 1]\r\n[1 -\u003e 3 -\u003e 4 -\u003e 2 -\u003e 1]\r\nThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 399.2px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 199.6px; transform-origin: 407px 199.6px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 225.5px 8px; transform-origin: 225.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an asymmetric adjacency matrix, determine the number of unique\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.5px 8px; transform-origin: 29.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edirected\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e cycles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the graph represented by adjacency matrix\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 0;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 0 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; tab-size: 4; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 0 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehas 7 cycles. They are:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 143.033px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 71.5167px; transform-origin: 404px 71.5167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 32px 8.5px; tab-size: 4; transform-origin: 32px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 52px 8.5px; tab-size: 4; transform-origin: 52px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[2 -\u0026gt; 4 -\u0026gt; 2]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 2 -\u0026gt; 4 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; 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min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; tab-size: 4; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e[1 -\u0026gt; 3 -\u0026gt; 4 -\u0026gt; 2 -\u0026gt; 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 380.5px 8px; transform-origin: 380.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function nCycles = count_directed_cycles(A)\r\n nCycles = 0;\r\nend","test_suite":"%%\r\nA = [\r\n 0 1 1 0\r\n 1 1 0 1\r\n 1 0 0 1\r\n 1 1 0 0];\r\nnCycles = 7;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 0 1 0 1 0 0\r\n 0 0 0 1 1 0 1 1\r\n 1 1 0 1 1 0 1 1\r\n 0 1 0 0 1 0 1 0\r\n 1 1 0 0 1 0 1 0\r\n 1 0 0 0 0 0 1 0\r\n 1 1 1 1 0 0 0 1\r\n 0 0 0 1 1 1 1 1];\r\nnCycles = 197;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 1 0 1 0 0 1 1 0 1 1\r\n 1 0 1 0 1 1 1 1 1 1\r\n 0 0 1 0 0 1 1 1 1 1\r\n 0 1 0 0 0 0 0 0 1 1\r\n 0 0 1 0 1 0 1 1 1 0\r\n 1 1 1 0 0 0 0 1 0 0\r\n 0 0 0 1 0 1 1 0 1 0\r\n 1 0 1 1 0 0 0 0 1 0\r\n 1 0 0 0 0 0 1 0 0 1\r\n 1 0 0 0 0 0 0 0 1 0];\r\nnCycles = 744;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 0 1 0 1 1 1 1\r\n 0 0 0 1 0 1 0 1 1 1\r\n 0 0 0 0 0 0 1 1 1 1\r\n 0 0 1 0 1 1 0 1 0 0\r\n 0 1 0 1 0 0 0 0 0 1\r\n 1 0 0 1 1 1 1 0 1 1\r\n 0 1 1 1 1 0 1 1 0 1\r\n 0 1 0 0 1 1 0 0 0 1\r\n 1 0 1 0 0 1 1 0 1 1\r\n 1 0 1 1 0 1 1 0 0 0];\r\nnCycles = 4961;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 1 0 0 0 1 0\r\n 1 0 0 0 1 1 1 1\r\n 0 0 0 0 0 1 0 1\r\n 1 1 0 0 1 1 1 0\r\n 1 1 0 0 0 0 0 1\r\n 0 0 1 0 1 0 0 0\r\n 0 1 0 0 0 1 0 1\r\n 0 0 1 1 0 1 0 0];\r\nnCycles = 116;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 1 0 1 1 1 0 0 1 0\r\n 1 0 0 1 1 0 1 0 0 1\r\n 0 1 1 0 1 0 0 0 1 1\r\n 0 1 1 0 1 1 0 0 1 0\r\n 1 0 0 0 0 1 1 0 0 1\r\n 0 1 1 0 0 1 0 0 0 1\r\n 0 1 1 0 1 0 1 1 1 1\r\n 1 1 1 0 0 0 0 0 1 1\r\n 1 1 1 0 1 1 1 1 0 1\r\n 0 0 1 1 1 0 1 0 1 0];\r\nnCycles = 5888;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n\r\n%%\r\nA = [\r\n 0 0 0 1 1 0 1 0 1 1 0 0\r\n 0 0 1 1 1 0 0 0 0 0 0 1\r\n 1 0 1 0 0 0 1 1 1 0 1 1\r\n 1 0 1 1 0 0 0 0 1 0 0 0\r\n 1 1 1 0 1 0 0 0 1 0 1 0\r\n 0 1 0 1 0 0 0 0 1 1 0 0\r\n 1 1 1 1 1 0 1 0 1 0 1 0\r\n 0 1 0 1 0 0 1 1 1 1 1 1\r\n 0 1 0 1 1 0 0 1 0 1 1 0\r\n 0 1 1 1 1 1 0 0 0 1 1 0\r\n 1 1 1 1 0 0 1 1 0 0 0 0\r\n 0 1 1 1 1 1 1 1 0 0 0 1];\r\nnCycles = 50093;\r\nassert(isequal(count_directed_cycles(A),nCycles))\r\n","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":1057,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":"2021-12-31T15:51:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T13:38:03.000Z","updated_at":"2026-04-04T08:59:38.000Z","published_at":"2013-01-04T13:48:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an asymmetric adjacency matrix, determine the number of unique\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edirected\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e cycles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the graph represented by adjacency matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [\\n 0 1 1 0;\\n 1 1 0 1;\\n 1 0 0 1;\\n 1 1 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehas 7 cycles. They are:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[2 -\u003e 2]\\n[1 -\u003e 2 -\u003e 1]\\n[1 -\u003e 3 -\u003e 1]\\n[2 -\u003e 4 -\u003e 2]\\n[1 -\u003e 2 -\u003e 4 -\u003e 1]\\n[1 -\u003e 3 -\u003e 4 -\u003e 1]\\n[1 -\u003e 3 -\u003e 4 -\u003e 2 -\u003e 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is an adjacency matrix of 0s and 1s, and the output should be the number of unique (simple) directed cycles in the graph.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56020,"title":"Easy Sequences 72: Complete Graph","description":"In Graph Theory, we say a graph is a Complete Graph, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\r\n \r\nIn this problem we are asked to calculate , which is the number of connections of a complete graph with nodes.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440001px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 406px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 203px; transform-origin: 407px 203px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn Graph Theory, we say a graph is a \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Complete_graph\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eComplete Graph\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 324px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 162px; text-align: left; transform-origin: 384px 162px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"359\" height=\"318\" style=\"vertical-align: 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 11px; text-align: left; transform-origin: 384px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem we are asked to calculate \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"18\" height=\"20\" style=\"width: 18px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, which is the number of connections of a complete graph with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e nodes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k = K(n)\r\n y = x;\r\nend","test_suite":"%%\r\nn = '12';\r\nk_correct = '66';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '1234';\r\nk_correct = '760761';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '123456789';\r\nk_correct = '7620789313366866';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '555555555555';\r\nk_correct = '154320987653734567901235';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '8888888888888888';\r\nk_correct = '39506172839506160493827160493828';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = '33333333333333333333';\r\nk_correct = '555555555555555555527777777777777777778';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = repmat('987654321',1,5);\r\nk_correct = '487730529870446579753162629635878679518594727932022558549306508666590458783874408901158360';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn = repmat('1',1,50);\r\nk_correct = '61728395061728395061728395061728395061728395061721604938271604938271604938271604938271604938271605';\r\nassert(isequal(K(n),k_correct))\r\n%%\r\nn1 = repmat('2',1,20);\r\nn2 = repmat('3',1,20);\r\nn3 = repmat('4',1,20);\r\nk_correct = [160 161 162 164 155 156 157 159 149 160 161 162 164 155 156 157 159 149 160 153 156 165 164 164 152 161 160 159 158 156 165 164 164 152 161 160 159 158 159];\r\nassert(isequal(K(n1)+K(n2)+K(n3),k_correct))\r\n%%\r\nfiletext = fileread('K.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'BigInteger') || contains(filetext, 'java') ...\r\n|| contains(filetext, 'sum') || contains(filetext, 'prod') || contains(filetext, 'if') || contains(filetext, 'for') ... \r\n|| contains(filetext, 'switch') || contains(filetext, 'while') || contains(filetext, 'try') || contains(filetext, 'repmat') ...\r\n|| contains(filetext, '{') || contains(filetext, '}') || contains(filetext, 'cell');\r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":255988,"edited_by":255988,"edited_at":"2022-09-22T13:34:32.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-22T09:44:22.000Z","updated_at":"2022-09-22T13:34:32.000Z","published_at":"2022-09-22T11:09:35.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn Graph Theory, we say a graph is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Complete_graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eComplete Graph\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, if all of its nodes are connected to evey other nodes in the graph. Examples of complete graphs are shown below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"318\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"359\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem we are asked to calculate \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eK_n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, which is the number of connections of a complete graph with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2732,"title":"Construct a precedence graph from a code segment","description":"A hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\r\n\r\nNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\r\n\r\nAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\r\n\r\nExample:\r\n\r\n C = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\n\r\nHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\r\n\r\nThus the resulting adjacency matrix will be as follows:\r\n\r\n \r\n mat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\n\r\n\r\n\r\n\r\nDefinition of adjacency matrix:\r\n\u003chttp://en.wikipedia.org/wiki/Adjacency_matrix\u003e","description_html":"\u003cp\u003eA hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\u003c/p\u003e\u003cp\u003eNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\u003c/p\u003e\u003cp\u003eAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eC = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\n\u003c/pre\u003e\u003cp\u003eHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\u003c/p\u003e\u003cp\u003eThus the resulting adjacency matrix will be as follows:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\n\u003c/pre\u003e\u003cp\u003eDefinition of adjacency matrix: \u003ca href = \"http://en.wikipedia.org/wiki/Adjacency_matrix\"\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/a\u003e\u003c/p\u003e","function_template":"function y = pGraph(x)\r\n\r\n\r\n\r\n\r\nend","test_suite":"%%\r\nC = {'a=1;'\r\n 'b=1;'\r\n 'c=a+b;'\r\n 'c=c+1;'};\r\nmat = [0 0 1 1;\r\n 0 0 1 1;\r\n 0 0 0 1;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n\r\n\r\n%%\r\nC = {'a=1;'\r\n 'a=1;'\r\n 'c=1;'\r\n 'c=1;'};\r\nmat = [0 0 0 0;\r\n 0 0 0 0;\r\n 0 0 0 0;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n%%\r\nC = {'a=1;'\r\n 'a=1;'\r\n 'c=a+1;'\r\n 'c=a+1;'};\r\nmat = [0 0 0 0;\r\n 0 0 1 1;\r\n 0 0 0 0;\r\n 0 0 0 0];\r\nassert(isequal(pGraph(C),mat))\r\n\r\n%%\r\nC = {'a=1;'\r\n 'b=a+2;'\r\n 'c=b+1;'\r\n 'd=c+1;'};\r\nmat = double(~tril(ones(4)))\r\nassert(isequal(pGraph(C),mat))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2014-12-06T07:56:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-12-06T07:54:40.000Z","updated_at":"2024-11-02T13:28:43.000Z","published_at":"2014-12-06T07:54:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA hypothetical MATLAB code segment containing n lines is given in the form of a cell array. The i-th cell contains the i-th line of the code. Each of the lines contains simple arithmetic expressions.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, construct an adjacency matrix of a graph containing n-vertices. The i-th vertex will represent the i-th line of the code. There should be a directed edge from i-th vertex to j-th vertex only if the values generated at i-th line are used in the j-th line.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll the variables in the code will have single letter names (e.g.: a,b,x,y etc).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = {'a=1;'\\n 'b=1;'\\n 'c=a+b;'\\n 'c=c+1;'};]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the cell array C contains a code segment. The first two lines are independent in the sense that they do not use values generated at any other lines. The third line uses information generated at line 1 and 2. The fourth line uses information generated at line 1,2 and 3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThus the resulting adjacency matrix will be as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mat = [0 0 1 1;\\n 0 0 1 1;\\n 0 0 0 1;\\n 0 0 0 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDefinition of adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Adjacency_matrix\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Adjacency_matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":283,"title":"Give the Shortest Path Through The Maze","description":"*Description*\r\n\r\nThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size _M_ x _N_ where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\r\n\r\n + +\r\n value = 0 -\u003e 00 -\u003e no walls -\u003e \r\n NW + + \r\n\r\n + +\r\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\r\n NW + +\r\n \r\n +---+\r\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \r\n NW + +\r\n\r\n +---+\r\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\r\n + +\r\n\r\n_Note: all outer boundaries are walls. My test cases provide for this already. You do *not* need to account for this._\r\n\r\nThe path will always start at the NorthWest corner (subscript |(1,1)|) and end at the SouthEast corner (subscript |(M,N)|). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\r\n\r\n path = [ 1 2 0 0 0 0 \r\n 4 3 0 9 10 0\r\n 5 6 7 8 11 0 \r\n 0 0 0 0 12 13 ]\r\n\r\nwhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\r\n\r\nYou are *NOT* guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\r\n\r\n*Example*\r\n\r\nInput maze:\r\n\r\n maze = [ 3 2 2 2 3\r\n 1 3 2 2 3\r\n 1 3 2 3 1\r\n 1 1 0 2 0\r\n 1 0 2 1 1 ];\r\n \r\nGraphical Representation:\r\n\r\n +---+---+---+---+---+\r\n | | |\r\n + +---+---+---+---+\r\n | | | |\r\n + +---+---+---+ +\r\n | | | | |\r\n + + + +---+ +\r\n | | |\r\n + + +---+ + +\r\n | | | |\r\n +---+---+---+---+---+\r\n\r\nSolution:\r\n\r\n soln = [ 1 0 0 0 0\r\n 2 0 0 0 0\r\n 3 0 0 0 0\r\n 4 7 8 9 10\r\n 5 6 0 0 11 ]\r\n\r\nGraphical Representation:\r\n\r\n \r\n +---+---+---+---+---+\r\n | 1 | |\r\n + +---+---+---+---+\r\n | 2 | | |\r\n + +---+---+---+ +\r\n | 3 | | | |\r\n + + + +---+ +\r\n | 4 | 7 8 9 10 |\r\n + + +---+ + +\r\n | 5 6 | |11 |\r\n +---+---+---+---+---+","description_html":"\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size \u003ci\u003eM\u003c/i\u003e x \u003ci\u003eN\u003c/i\u003e where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\u003c/p\u003e\u003cpre\u003e + +\r\n value = 0 -\u003e 00 -\u003e no walls -\u003e \r\n NW + + \u003c/pre\u003e\u003cpre\u003e + +\r\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\r\n NW + +\u003c/pre\u003e\u003cpre\u003e +---+\r\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \r\n NW + +\u003c/pre\u003e\u003cpre\u003e +---+\r\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\r\n + +\u003c/pre\u003e\u003cp\u003e\u003ci\u003eNote: all outer boundaries are walls. My test cases provide for this already. You do \u003cb\u003enot\u003c/b\u003e need to account for this.\u003c/i\u003e\u003c/p\u003e\u003cp\u003eThe path will always start at the NorthWest corner (subscript \u003ctt\u003e(1,1)\u003c/tt\u003e) and end at the SouthEast corner (subscript \u003ctt\u003e(M,N)\u003c/tt\u003e). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\u003c/p\u003e\u003cpre\u003e path = [ 1 2 0 0 0 0 \r\n 4 3 0 9 10 0\r\n 5 6 7 8 11 0 \r\n 0 0 0 0 12 13 ]\u003c/pre\u003e\u003cp\u003ewhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\u003c/p\u003e\u003cp\u003eYou are \u003cb\u003eNOT\u003c/b\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInput maze:\u003c/p\u003e\u003cpre\u003e maze = [ 3 2 2 2 3\r\n 1 3 2 2 3\r\n 1 3 2 3 1\r\n 1 1 0 2 0\r\n 1 0 2 1 1 ];\u003c/pre\u003e\u003cp\u003eGraphical Representation:\u003c/p\u003e\u003cpre\u003e +---+---+---+---+---+\r\n | | |\r\n + +---+---+---+---+\r\n | | | |\r\n + +---+---+---+ +\r\n | | | | |\r\n + + + +---+ +\r\n | | |\r\n + + +---+ + +\r\n | | | |\r\n +---+---+---+---+---+\u003c/pre\u003e\u003cp\u003eSolution:\u003c/p\u003e\u003cpre\u003e soln = [ 1 0 0 0 0\r\n 2 0 0 0 0\r\n 3 0 0 0 0\r\n 4 7 8 9 10\r\n 5 6 0 0 11 ]\u003c/pre\u003e\u003cp\u003eGraphical Representation:\u003c/p\u003e\u003cpre\u003e +---+---+---+---+---+\r\n | 1 | |\r\n + +---+---+---+---+\r\n | 2 | | |\r\n + +---+---+---+ +\r\n | 3 | | | |\r\n + + + +---+ +\r\n | 4 | 7 8 9 10 |\r\n + + +---+ + +\r\n | 5 6 | |11 |\r\n +---+---+---+---+---+\u003c/pre\u003e","function_template":"function path = solve_maze(maze)\r\n path = zeros( size( maze ) );\r\nend","test_suite":"%%\r\nassert(isequal(solve_maze([3 2;1 2]),[1 0;2 3]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2;3 3 1;1 1 1]),[1 2 3;0 0 4;0 0 5]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 3 2 2 2;1 0 1 2 1;3 2 3 3 0;1 0 2 2 0;1 1 1 0 1]),[1 4 5 6 7;2 3 0 0 8;0 0 0 0 9;0 0 0 0 10;0 0 0 0 11]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 3 3 2 3;3 1 2 1 1 3 1 3;1 0 3 0 0 1 3 3;1 1 0 2 0 0 1 1;3 1 3 3 2 1 2 1;3 2 2 1 3 0 1 0;3 3 2 3 1 2 2 0;1 3 3 0 0 2 0 2;1 2 3 1 0 2 1 3;1 1 3 2 3 1 1 0]),[1 2 0 0 0 0 0 0;0 3 0 0 0 0 0 0;0 4 0 0 0 0 0 0;0 5 6 7 8 9 0 0;0 0 0 0 0 10 11 0;0 0 0 0 12 11 12 13;0 0 0 0 13 14 15 14;0 0 0 0 14 15 16 0;0 0 0 0 0 0 17 0;0 0 0 0 0 0 18 19]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 2 3 3 2 3 3 3 2 3 2 3 2 3;3 0 3 3 1 2 2 3 3 0 2 1 0 1 2 2 0;1 0 2 3 2 1 1 0 0 3 1 0 2 1 1 0 2;3 1 1 2 0 0 2 0 2 3 3 1 0 3 1 0 3;1 0 1 0 3 0 1 0 0 2 2 1 0 2 1 2 1;1 2 0 0 3 1 2 1 2 3 1 1 0 1 3 1 1;3 1 1 3 2 2 2 2 2 1 2 3 2 0 3 1 2;1 3 0 1 0 3 0 2 1 3 2 2 2 1 1 1 2;1 3 3 1 1 1 2 3 2 2 2 2 3 3 2 0 1;3 1 3 0 2 0 2 0 1 0 3 1 2 3 2 0 0;1 1 0 2 3 3 1 2 2 1 3 0 2 3 2 2 0;3 0 0 2 3 3 0 0 2 2 0 0 1 3 1 3 1]),[1 2 0 0 0 0 0 0 0 0 0 0 21 22 0 0 0;0 3 0 0 0 0 0 0 14 15 16 19 20 23 24 0 0;0 4 5 0 0 0 0 12 13 0 17 18 0 0 25 0 0;0 0 6 7 8 9 10 11 0 0 0 0 0 0 26 0 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 28 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 29 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 32;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 33;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 34;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 3 3 2 2 2 3 2 3 3 2 2 2 2 2 2 2 2 3 3 2 3 2 3;1 2 2 2 1 1 1 3 0 3 1 1 0 2 3 0 2 0 0 3 3 0 3 1 3;3 1 3 2 3 1 2 2 2 3 3 0 3 3 3 3 3 3 0 0 2 0 3 0 3;3 1 1 1 3 3 3 1 0 1 1 3 3 2 2 2 3 1 3 1 3 1 3 1 0;3 1 3 0 1 3 2 1 1 2 1 0 3 1 3 2 1 0 1 0 2 0 3 2 0;3 0 2 2 2 3 2 1 2 2 0 3 0 0 1 1 0 1 3 1 3 0 2 0 0;3 3 0 0 2 0 3 0 1 1 2 3 1 0 3 1 3 3 0 1 2 3 1 3 2;1 0 2 0 3 1 1 0 3 2 0 0 0 0 0 0 1 3 3 2 2 0 0 2 2;1 2 1 3 2 1 3 1 0 3 2 3 3 3 1 0 1 2 0 1 3 3 0 2 1;1 0 3 2 0 0 3 3 2 2 0 0 3 3 0 1 2 0 2 1 2 0 2 3 3;3 0 2 3 3 2 3 2 1 3 3 1 2 0 3 2 1 0 2 0 1 3 1 2 0;1 0 1 1 2 2 1 2 2 1 3 1 0 3 3 2 3 2 1 2 3 3 3 1 2;1 2 1 0 0 3 3 3 0 1 2 1 1 2 2 0 2 3 3 3 1 0 3 1 3;3 3 2 3 1 2 2 2 1 0 2 0 3 1 3 0 0 2 1 3 0 1 3 0 1;1 2 2 0 0 3 1 1 1 3 1 0 3 3 1 0 2 0 3 3 1 0 0 0 3;1 2 1 2 1 1 0 0 2 0 3 0 0 1 0 0 1 3 0 0 1 3 2 0 1;3 3 1 2 0 1 1 1 1 2 2 0 1 2 1 1 3 0 0 0 1 1 3 0 0;3 2 2 0 0 0 0 3 2 2 1 1 0 0 2 3 1 3 3 3 0 3 1 3 0;1 2 2 0 3 1 2 0 3 1 3 2 3 3 2 1 2 3 3 2 2 1 2 0 0;1 3 1 3 2 1 3 1 2 1 0 0 0 2 1 3 0 3 0 2 3 0 0 0 0]),[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 7 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 10 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;12 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;13 14 0 0 0 0 0 0 0 0 0 0 0 0 43 44 0 0 0 0 0 0 0 0 0;14 15 0 0 0 0 0 0 0 0 0 0 0 41 42 45 46 47 0 51 52 53 54 0 0;0 16 17 0 0 0 0 0 0 0 0 38 39 40 0 0 47 48 49 50 0 0 55 56 0;0 0 18 0 0 0 0 0 0 0 0 37 38 0 0 0 0 0 0 0 0 0 0 57 0;0 0 19 20 21 0 0 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0 58 0;0 0 0 0 22 23 24 25 0 0 0 35 0 0 0 0 0 0 0 0 0 0 0 59 0;0 0 0 0 0 0 25 26 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 60 0;0 0 0 0 0 0 26 27 28 0 0 33 0 0 0 0 0 0 0 0 0 0 0 61 0;0 0 0 0 0 0 0 0 29 30 31 32 0 0 0 0 0 0 0 0 0 0 0 62 63;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 66]))\r\n\r\n%%\r\nassert(isequal(solve_maze([3 2 2 3 3 3 2 3 2 3 2 3 2 3 2 2 2 2 3 3 3 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 3 3 3 2 2 3 2 3;3 2 0 1 3 2 1 0 1 2 2 1 2 1 1 0 2 1 0 2 1 3 3 2 3 3 0 0 3 2 3 1 3 1 3 3 0 3 1 0 0 2 1 1 0;1 1 3 0 3 3 0 1 2 2 3 0 0 2 2 3 3 0 0 1 3 3 3 1 3 2 3 1 3 3 1 0 0 0 0 2 2 2 0 3 3 0 1 1 2;1 0 2 2 0 1 0 2 2 3 2 1 0 3 1 3 1 1 1 3 3 2 1 1 2 0 1 1 2 1 3 0 3 3 0 1 1 2 2 3 1 1 1 1 3;1 1 0 0 0 3 0 2 2 0 0 2 1 3 2 3 2 1 3 2 0 2 3 0 0 0 3 0 0 1 0 1 0 2 0 1 1 1 2 3 0 2 1 3 1;3 3 1 1 2 0 3 0 0 3 2 1 1 1 3 1 3 0 1 0 1 0 3 1 3 1 2 3 1 2 1 1 3 3 2 1 2 1 3 0 2 3 2 0 2;3 1 2 0 0 3 3 1 1 2 1 0 3 3 3 0 2 1 3 0 3 0 0 2 2 3 2 1 0 1 3 2 0 2 3 3 3 0 1 2 1 1 1 0 2;3 1 2 0 0 3 1 0 3 3 2 3 2 0 2 3 2 2 0 0 2 0 1 1 3 1 1 3 2 0 1 0 0 2 2 1 2 1 0 0 3 0 2 2 1;3 0 0 2 0 0 1 0 3 2 2 0 0 3 1 1 2 0 0 0 3 2 0 2 1 1 2 0 3 2 3 2 0 3 3 3 1 3 2 1 0 2 0 1 2;1 0 1 0 1 2 3 2 0 1 0 0 0 1 1 2 1 3 0 2 0 0 0 3 2 0 0 1 2 0 2 1 2 3 2 1 0 3 2 0 2 1 1 2 2;3 0 0 1 0 2 0 2 0 2 3 2 3 0 2 3 3 1 0 2 3 2 0 3 0 0 2 0 2 3 0 3 1 0 3 1 0 2 2 1 0 1 2 2 3;1 3 3 1 2 3 1 2 2 3 3 3 3 0 2 1 2 1 1 3 0 2 2 2 0 3 3 2 0 3 0 0 0 0 3 2 2 0 0 1 3 3 0 0 0;1 0 3 1 2 1 0 3 0 0 0 0 3 2 3 0 2 2 0 0 1 1 1 2 3 3 1 0 2 1 2 1 3 1 2 0 2 3 0 0 0 3 1 0 3;3 2 0 0 1 1 2 3 1 1 2 1 2 2 0 3 2 1 2 0 0 1 3 1 3 2 2 3 3 3 3 0 3 1 2 0 0 1 1 3 3 2 2 1 2;1 3 3 1 3 0 0 2 0 3 0 3 1 2 2 3 0 3 0 2 0 3 1 1 0 2 3 2 1 2 0 0 2 0 3 1 0 3 2 1 0 1 1 1 2;3 1 3 3 2 1 2 1 1 3 1 3 1 0 3 3 2 3 1 3 0 0 1 2 1 3 0 2 2 2 2 1 2 3 2 0 3 2 0 2 1 2 1 1 2;1 2 2 1 3 2 0 3 1 1 1 0 2 3 1 2 0 0 2 0 0 2 3 2 3 1 1 3 2 1 1 3 3 3 0 1 3 3 1 0 2 0 1 1 0;1 2 0 0 2 1 1 0 2 2 0 2 0 3 1 2 2 0 2 0 3 2 0 0 2 1 1 1 0 1 2 0 0 2 3 1 0 0 1 2 0 3 2 1 2;1 2 2 1 0 2 1 1 3 1 2 3 1 2 2 3 1 2 2 2 1 1 0 0 1 2 0 2 0 3 2 1 0 2 3 1 1 0 2 1 1 0 0 0 2;3 0 2 0 1 1 2 0 3 3 3 3 0 1 0 1 3 1 1 2 1 1 1 3 0 0 3 0 0 3 1 0 2 3 2 0 2 2 0 3 0 1 1 3 0;3 1 3 0 2 1 3 1 0 3 0 3 1 2 1 3 2 3 0 1 0 3 3 3 0 1 2 0 0 1 2 2 3 2 0 2 1 2 2 3 1 0 0 3 1;3 1 2 0 1 2 3 0 1 0 0 1 2 0 1 1 1 2 1 1 3 1 1 2 0 1 3 0 3 0 0 0 0 1 1 2 1 3 0 2 0 3 2 2 1;3 0 2 1 0 3 2 2 1 0 2 3 1 1 2 1 1 1 1 3 3 2 2 3 2 1 2 2 0 1 3 3 0 3 3 0 1 2 0 0 3 2 1 0 1;3 3 2 2 3 3 2 0 2 1 1 3 3 3 1 2 0 3 0 1 1 3 3 1 0 3 0 1 0 2 1 2 2 0 0 0 2 1 2 1 0 3 0 3 2;1 0 3 2 1 0 2 3 1 1 3 1 3 2 2 2 3 0 2 1 3 2 1 1 1 3 1 0 0 0 0 0 3 2 3 3 3 1 2 2 1 2 0 3 0;1 0 3 3 3 2 2 3 0 0 0 3 3 3 0 0 3 3 0 0 2 3 1 3 3 3 1 1 3 2 3 0 0 2 2 1 0 0 2 3 3 3 3 3 0;3 0 1 1 0 1 2 3 1 3 1 3 0 3 3 1 2 3 3 2 3 3 2 3 1 3 2 1 0 1 0 1 3 1 0 2 2 0 0 3 1 0 3 0 0;1 3 3 2 2 2 1 0 1 0 3 3 2 0 2 3 0 1 0 0 3 1 0 1 2 1 0 2 1 3 2 3 2 0 0 2 0 3 2 1 3 1 0 0 3;3 2 3 2 2 3 1 3 0 1 2 0 3 3 2 3 3 1 0 1 1 1 0 2 2 2 3 3 2 0 2 0 1 1 0 2 0 1 0 2 1 0 3 2 0;3 0 3 1 0 3 3 3 0 2 1 2 2 2 0 2 0 3 1 1 0 2 2 0 0 2 2 1 0 1 0 0 2 3 3 0 0 0 0 2 2 0 3 2 0;3 1 3 0 1 2 3 3 1 1 3 1 2 0 1 1 0 3 1 1 0 2 3 1 3 2 1 1 3 0 3 3 3 0 1 3 2 0 0 3 2 3 2 0 1;3 2 2 2 2 3 3 0 1 2 1 2 3 3 2 2 2 2 3 0 1 2 0 3 2 2 3 0 2 2 3 1 1 0 0 3 2 3 0 0 1 2 2 0 0;3 1 3 2 1 3 1 2 3 2 2 2 2 0 1 3 0 2 3 0 0 3 3 2 3 3 1 2 1 1 0 0 3 2 0 3 0 2 1 0 2 3 2 1 3;3 1 0 0 1 2 3 2 2 3 1 0 1 2 2 0 3 2 1 0 1 1 0 2 1 3 0 1 1 0 1 2 1 3 1 3 3 0 2 0 0 1 0 0 2;3 0 0 3 0 2 2 1 1 0 0 2 0 3 0 2 1 0 3 2 0 3 3 3 3 0 3 0 0 1 2 0 1 3 2 3 0 3 0 2 3 1 2 0 0;3 3 0 2 0 0 2 0 0 0 1 2 2 0 0 1 0 2 2 1 0 2 2 2 3 3 2 3 0 2 0 2 1 2 0 1 1 3 3 0 3 2 1 2 3;3 2 0 2 1 3 3 0 3 0 1 1 0 3 0 2 0 3 1 2 0 0 3 2 2 1 1 2 1 0 2 0 0 1 3 2 2 2 2 1 2 1 2 3 1;1 3 1 2 2 0 2 0 3 3 3 2 1 0 2 3 1 2 3 3 2 3 2 1 2 0 3 3 1 1 1 0 3 2 0 0 1 2 2 2 0 2 1 3 3;1 3 3 3 0 0 3 2 2 1 2 0 2 3 1 2 0 2 1 3 3 2 0 1 3 0 0 2 3 1 3 3 1 0 0 0 2 2 3 3 2 0 1 2 1;3 3 2 0 0 0 1 2 0 0 3 1 2 1 0 1 0 2 1 0 2 1 2 2 2 1 0 1 1 3 1 3 2 3 3 1 3 2 0 3 3 0 2 2 2;1 1 1 0 3 1 0 0 3 2 3 2 2 0 2 2 3 0 1 2 0 1 3 3 2 1 3 1 1 0 2 1 0 0 0 0 2 3 1 3 0 1 1 2 0;3 1 0 1 0 1 3 3 2 2 3 2 1 3 2 0 1 1 2 0 0 2 2 2 2 1 1 3 3 1 0 3 0 2 0 1 2 2 2 2 2 0 1 3 1;1 0 3 1 1 2 3 1 3 1 0 1 1 1 3 0 2 3 1 3 1 0 3 3 3 2 0 1 3 3 1 3 0 0 2 3 0 2 3 1 3 2 1 3 2;3 0 3 2 3 3 2 3 3 1 0 0 3 2 0 1 0 3 1 1 2 0 0 3 2 1 1 2 2 1 1 1 2 1 3 1 3 3 3 2 0 1 0 2 2;1 3 3 1 3 2 2 3 2 0 2 3 3 1 3 1 0 3 0 1 2 2 1 1 1 2 2 2 0 3 0 2 1 0 3 1 2 0 1 0 0 3 0 3 0;3 2 3 1 0 0 0 0 2 3 1 3 3 2 2 1 2 1 3 2 2 1 0 2 2 1 0 3 1 3 1 3 2 0 1 3 2 2 3 0 2 1 3 1 3;1 3 3 1 3 0 3 1 0 3 1 1 2 0 3 2 1 2 1 2 3 0 1 2 3 0 1 2 1 0 0 3 1 3 0 2 3 2 2 2 0 1 0 3 3;3 0 0 0 0 0 3 0 0 1 3 0 1 3 3 3 1 0 1 2 1 0 1 0 3 1 0 1 2 1 3 1 0 2 0 3 0 3 0 2 2 0 2 0 3;3 2 0 3 2 3 3 1 1 3 0 0 0 3 3 2 2 3 1 3 3 2 1 3 3 2 0 2 0 3 2 0 1 1 2 2 1 1 2 0 0 2 0 1 1;1 1 1 2 0 1 0 0 2 0 2 2 3 3 3 3 0 3 0 3 1 3 3 1 3 0 3 3 0 1 0 1 1 2 2 0 3 3 0 3 0 3 3 0 0]),[1 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 8 9 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 11 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;0 0 0 12 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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110]))","published":true,"deleted":false,"likes_count":14,"comments_count":0,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":57,"test_suite_updated_at":"2012-02-08T22:51:09.000Z","rescore_all_solutions":false,"group_id":33,"created_at":"2012-02-08T03:01:26.000Z","updated_at":"2026-02-07T15:43:43.000Z","published_at":"2012-02-09T00:21:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe purpose of this problem is to give the shortest path through a maze. The maze will be provided in a codified matrix of size\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e where each element of the matrix represents a place in the grid and the value of each element is a binary-code that represents the presence of walls. That is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ + +\\n value = 0 -\u003e 00 -\u003e no walls -\u003e \\n NW + + \\n\\n + +\\n value = 1 -\u003e 01 -\u003e wall to W -\u003e |\\n NW + +\\n\\n +---+\\n value = 2 -\u003e 10 -\u003e wall to N -\u003e \\n NW + +\\n\\n +---+\\n value = 3 -\u003e 11 -\u003e walls to N and W -\u003e |\\n + +]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote: all outer boundaries are walls. My test cases provide for this already. You do\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e need to account for this.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe path will always start at the NorthWest corner (subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(1,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) and end at the SouthEast corner (subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(M,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e). The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. For example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ path = [ 1 2 0 0 0 0 \\n 4 3 0 9 10 0\\n 5 6 7 8 11 0 \\n 0 0 0 0 12 13 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich represents an output solution that is 13 units long. As you can see, the NorthWest corner will always be 1 and the SouthEast corner will always be the length of your path.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput maze:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ maze = [ 3 2 2 2 3\\n 1 3 2 2 3\\n 1 3 2 3 1\\n 1 1 0 2 0\\n 1 0 2 1 1 ];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGraphical Representation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ +---+---+---+---+---+\\n | | |\\n + +---+---+---+---+\\n | | | |\\n + +---+---+---+ +\\n | | | | |\\n + + + +---+ +\\n | | |\\n + + +---+ + +\\n | | | |\\n +---+---+---+---+---+]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolution:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ soln = [ 1 0 0 0 0\\n 2 0 0 0 0\\n 3 0 0 0 0\\n 4 7 8 9 10\\n 5 6 0 0 11 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGraphical Representation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ +---+---+---+---+---+\\n | 1 | |\\n + +---+---+---+---+\\n | 2 | | |\\n + +---+---+---+ +\\n | 3 | | | |\\n + + + +---+ +\\n | 4 | 7 8 9 10 |\\n + + +---+ + +\\n | 5 6 | |11 |\\n +---+---+---+---+---+]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44378,"title":"Five-dimensional maze","description":"*Description*\r\n\r\nThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes _x_ and _y_. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\r\n\r\nThis problem is a generalization of \u003chttps://www.mathworks.com/matlabcentral/cody/problems/283 Problem 283\u003e. If you haven't solved that yet, I would recommend solving it first.\r\n\r\n*Encoding*\r\n\r\n* The maze will be represented by an [ _M_ x _N_ x _O_ x _P_ x _Q_ ] matrix. \r\n* Each element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\r\n* Walls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\r\n* The start position is at the origin: subscript |(1,1,1,1,1)|.\r\n* The end position is at the furthest extent: subscript |(M,N,O,P,Q)|.\r\n* The output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to \u003chttps://www.mathworks.com/matlabcentral/cody/problems/283 Problem 283\u003e for a 2-D example.\r\n* You are *NOT* guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.","description_html":"\u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes \u003ci\u003ex\u003c/i\u003e and \u003ci\u003ey\u003c/i\u003e. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\u003c/p\u003e\u003cp\u003eThis problem is a generalization of \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/283\"\u003eProblem 283\u003c/a\u003e. If you haven't solved that yet, I would recommend solving it first.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEncoding\u003c/b\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003eThe maze will be represented by an [ \u003ci\u003eM\u003c/i\u003e x \u003ci\u003eN\u003c/i\u003e x \u003ci\u003eO\u003c/i\u003e x \u003ci\u003eP\u003c/i\u003e x \u003ci\u003eQ\u003c/i\u003e ] matrix.\u003c/li\u003e\u003cli\u003eEach element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\u003c/li\u003e\u003cli\u003eWalls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\u003c/li\u003e\u003cli\u003eThe start position is at the origin: subscript \u003ctt\u003e(1,1,1,1,1)\u003c/tt\u003e.\u003c/li\u003e\u003cli\u003eThe end position is at the furthest extent: subscript \u003ctt\u003e(M,N,O,P,Q)\u003c/tt\u003e.\u003c/li\u003e\u003cli\u003eThe output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/283\"\u003eProblem 283\u003c/a\u003e for a 2-D example.\u003c/li\u003e\u003cli\u003eYou are \u003cb\u003eNOT\u003c/b\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/li\u003e\u003c/ul\u003e","function_template":"function path = solve_maze5(maze)\r\n path = zeros(size(maze));\r\n path(1) = 1;\r\nend","test_suite":"%%\r\nmaze = reshape([15 15 15 15 15 15 15 15 15 31], [1 1 1 1 10]);\r\ntruth = reshape([1 2 3 4 5 6 7 8 9 10], [1 1 1 1 10]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([28 28 30 28 30 30 29 30 28 29 29 30 31 29 28 29 30 30 31 29 29 28 30 29 29 29 28 30 31 29 29 30 29 31 29 29 30 30 28 31 30 29 28 30 31 30 30 29 30 29 28 31 30 29 28 30 29 29 28 31 29 28 30 31 30 29 30 31 29 29 29 29 28 30 30 31 28 30 31 29 29 31 29 28 29 28 31 30 30 29 30 30 31 31 30 30 30 30 30 31], [10 10 1 1 1]);\r\ntruth = reshape([1 2 3 4 5 6 7 0 11 12 0 0 0 0 0 0 8 9 10 13 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 16 15 0 0 0 0 0 0 0 0 17 18 0 0 0 0 0 0 0 0 20 19 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 24 23 22 0 0 0 0 0 0 26 25 0 0 0 0 0 0 0 0 27 28 29 30 31], [10 10 1 1 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([20 29 22 23 23 23 22 30 29 21 28 29 23 29 29 29 30 29 29 29 23 22 31 30 31 23 23 29 23 21 29 29 23 22 31 23 23 22 22 31 28 23 21 28 29 31 23 30 31 23 30 22 31 23 23 28 30 31 29 21 31 29 31 21 29 22 31 29 29 29 23 23 23 31 23 22 31 22 31 31 28 23 28 23 23 23 22 31 30 23 28 23 23 28 23 31 30 31 23 23 28 31 29 28 29 31 29 29 31 31 29 31 30 29 31 29 30 31 30 31 30 30 30 31 31], [5 5 1 5 1]);\r\ntruth = reshape([1 2 0 0 0 0 3 0 0 0 13 14 0 0 0 12 15 16 0 0 11 18 17 0 0 0 0 0 0 0 0 4 0 0 0 0 5 0 0 0 9 8 25 28 29 10 19 26 27 30 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 7 24 0 0 0 20 23 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 22 0 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33], [5 5 1 5 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([18 23 19 28 27 22 23 22 31 19 25 22 29 25 21 27 25 29 27 29 30 30 31 22 31 29 23 29 29 29 29 21 29 21 31 31 30 31 31 29 29 29 21 29 29 23 23 23 30 31 23 23 29 27 19 27 27 31 22 31 30 23 28 23 27 22 29 29 19 25 19 30 31 27 23 30 22 30 28 31 29 31 21 23 23 30 29 29 21 29 22 31 31 31 31 31 30 31 30 23 27 23 22 23 29 17 19 29 23 29 25 23 19 27 29 31 22 30 31 29 18 31 19 27 31 23 31 21 28 29 31 23 31 31 29 30 29 29 31 29 23 29 30 28 31 23 30 31 31 23 19 27 29 23 27 31 30 31 27 25 19 27 30 30 31 22 31 21 26 27 31 19 31 22 23 31 30 31 22 29 28 31 28 31 31 31 29 23 21 21 23 30 23 31 31 23 23 22 30 31 29 26 31 30 29 27 26 30 29 29 30 30 30 31 27 29 26 31 25 25 30 31 27 31 31 30 30 28 31 29 30 31 30 30 31 28 29 30 31 31 31 31 30 31 29 30 31 30 30 31], [5 5 2 5 1]);\r\ntruth = reshape([1 2 0 0 0 0 0 0 0 0 0 24 23 0 0 0 19 22 0 0 0 20 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 13 0 0 0 17 14 0 0 0 3 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 11 0 0 0 0 12 0 0 0 16 15 0 0 0 4 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 35 36 0 6 7 0 0 0 0 0 0 0 0 28 0 0 0 0 29 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 38 0 0 0 34 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 32 39 0 0 0 0 40], [5 5 2 5 1]);\r\nassert(isequal(solve_maze5(maze), truth));\r\n\r\n%%\r\nmaze = reshape([21 5 22 29 19 23 31 7 30 15 21 26 23 7 19 23 22 29 11 11 14 15 30 30 23 11 7 11 25 15 13 13 15 15 27 23 31 28 29 27 13 14 31 31 15 23 14 23 30 19 30 30 31 29 25 7 14 29 23 31 25 27 30 29 23 30 29 12 31 19 7 23 31 19 11 28 30 15 26 27 15 13 25 26 23 27 23 23 28 15 19 20 23 27 15 7 31 19 23 27 28 23 22 31 15 30 23 29 30 15 15 28 31 23 21 23 30 23 29 23 22 15 15 23 23 26 15 23 7 29 23 28 31 25 15 23 27 27 31 23 29 23 14 29 25 23 7 15 23 31 27 30 15 25 21 28 23 19 31 15 31 27 23 19 23 22 30 30 27 15 30 15 27 7 23 30 27 26 31 25 23 19 30 31 23 29 29 14 31 27 23 30 23 15 23 23 23 30 31 19 7 23 23 25 27 26 27 23 13 23 21 31 23 27 15 31 15 29 26 23 15 19 31 23 15 29 23 30 31 15 31 31 7 28 31 14 23 13 23 15 29 21 31 30 31 31 31 23 30 31 26 27 25 31 21 15 21 31 28 31 15 23 21 29 29 26 31 29 27 29 30 31 27 15 15 27 23 23 13 27 25 23 23 29 19 30 27 27 31 19 31 28 27 15 29 15 23 23 23 15 15 26 31 5 15 31 30 15 29 31 26 31 30 31 29 15 13 31 17 31 27 31 27 15 15 27 31 30 30 27 11 13 23 19 29 31 27 29 14 31 28 29 27 27 15 31 31 27 30 23 30 31 15 13 15 15 15 29 31 13 13 31 15 31 29 29 29 23 23 23 15 23 30 23 7 22 27 7 19 29 25 30 30 30 31 23 29 23 30 25 11 29 7 22 31 26 31 26 30 27 29 31 27 28 27 15 15 27 27 27 28 30 27 19 31 31 14 15 27 21 30 31 31 23 31 19 28 31 27 15 21 21 23 31 29 27 31 29 23 15 22 29 29 31 25 27 23 15 7 15 29 25 13 30 29 15 27 21 27 23 30 15 29 26 23 28 29 15 22 31 27 23 3 14 31 13 15 14 30 29 29 15 23 23 29 31 14 30 31 23 22 23 15 15 29 23 15 23 15 31 15 30 28 30 29 28 27 31 11 27 31 13 30 15 11 13 28 31 15 11 15 27 14 30 15 28 27 28 15 11 30 31 15 29 13 28 30 27 31 31 30 30 30 27 31 30 31 26 31 15 15 29 28 15 27 25 31 31 13 13 31 26 31 31 29 29 27 26 31 29 27 30 31 30 31 25 28 27 30 31 31 27 27 28 31 26 31 26 31 15 26 29 30 31 27 15 31 31 14 27 29 15 15 14 15 30 31 31 29 15 30 15 30 31 15 31 29 30 30 15 15 15 15 14 31 25 30 30 18 31 15 21 23 14 31 7 30 29 15 13 14 27 27 23 31 23 27 7 26 27 31 27 23 31 29 29 15 15 15 29 30 15 19 21 15 29 31 29 29 27 15 31 23 31 23 21 29 13 22 15 31 31 27 25 11 14 29 29 22 31 28 31 31 11 27 31 14 30 30 31 7 22 31 25 13 23 25 27 23 31 25 23 25 22 31 29 28 31 27 31 31 27 30 26 15 21 23 21 29 15 15 23 31 15 23 31 13 30 30 23 28 31 21 15 13 23 31 31 22 31 19 31 7 30 23 26 31 30 29 15 30 15 22 31 15 19 28 31 30 23 23 31 26 31 7 15 7 27 14 29 29 26 30 27 31 23 26 31 23 25 30 30 14 31 31 26 31 27 30 31 23 11 31 15 21 7 23 28 31 23 26 31 31 31 27 26 30 30 31 25 30 23 31 14 31 31 31 21 26 15 30 30 31 31 7 15 23 28 31 31 15 29 27 13 15 15 30 30 31 27 29 23 29 29 15 23 23 31 30 31 30 23 30 29 31 28 31 31 15 15 15 14 15 31 15 30 23 27 7 17 30 15 19 13 31 27 7 23 30 23 31 22 27 29 27 30 23 22 31 31 22 31 23 15 15 22 29 15 19 11 31 23 19 30 29 22 23 27 23 31 19 19 22 15 31 31 11 19 31 31 31 29 30 31 31 19 29 29 14 29 29 31 31 25 29 31 31 19 31 31 27 29 31 11 19 29 31 28 30 31 31 29 23 30 15 14 29 13 23 29 7 27 31 14 31 22 30 31 31 31 31 15 7 13 15 15 29 30 31 23 7 23 15 14 15 15 31 14 15 23 21 23 31 27 27 30 23 27 28 27 27 27 15 29 27 27 29 21 30 27 27 31 31 14 27 15 11 23 27 23 31 23 27 23 15 15 31 30 15 23 31 31 15 23 27 15 27 30 30 31 25 27 30 31 23 30 29 30 27 27 15 30 27 14 31 14 23 27 13 27 7 27 31 31 23 15 27 23 19 15 29 22 30 31 25 27 23 31 25 27 21 7 23 31 23 31 15 27 15 15 21 31 15 29 15 15 29 29 15 7 15 31 29 23 29 30 31 31 23 7 14 31 30 15 15 31 30 30 30 29 25 15 11 15 15 27 29 29 29 31 30 31 31 31 15 15 14 31 11 27 28 27 27 29 29 31 27 31 31 31 29 28 30 29 15 27 30 29 31 9 14 27 27 15 31 15 15 30 31 27 11 29 13 15 29 13 30 31 25 15 31 30 30 31 29 15 30 31 11 31 13 14 31 30 31 31 15 28 30 29 26 31 27 27 15 30 31 30 15 29 15 30 11 31 27 29 14 31 29 15 31 14 29 15 31 30 31 31 31 15 28 30 30 31 15 31 15 31 30 31 7 28 30 15 13 15 30 23 15 15 15 21 21 30 31 29 23 27 7 27 23 23 23 22 15 24 30 29 19 21 21 15 31 27 23 31 30 29 28 31 22 15 31 29 29 30 23 30 23 23 31 11 27 15 23 25 28 15 29 29 31 30 23 29 29 23 11 19 31 27 19 27 19 26 27 23 23 29 7 23 23 7 30 29 9 30 11 15 23 15 25 27 30 15 15 31 27 15 27 31 14 30 29 14 31 29 13 29 29 31 23 31 15 23 7 30 31 21 23 15 23 15 30 31 15 30 27 30 15 19 22 27 30 28 31 13 31 29 30 31 29 27 30 31 13 31 30 27 23 31 27 31 21 31 31 27 15 27 14 31 22 15 11 25 19 30 31 27 30 23 19 23 30 31 15 23 29 26 31 29 15 27 30 29 15 28 27 31 31 27 23 21 14 30 29 31 23 23 23 15 29 30 31 27 31 31 31 19 26 31 15 15 28 31 23 30 29 23 29 23 30 31 26 31 23 22 29 29 29 29 28 31 29 31 15 31 22 31 30 29 14 30 31 31 31 31 23 31 14 31 11 15 11 23 29 30 31 22 31 23 29 29 19 24 27 26 31 22 31 13 26 15 14 31 31 30 29 31 23 11 13 30 31 14 31 31 7 23 31 31 14 31 11 25 27 31 15 23 31 19 27 15 7 13 19 28 15 25 15 7 30 27 27 27 15 30 31 15 23 15 14 31 27 30 31 27 19 29 29 31 21 7 31 30 23 30 31 27 27 31 30 29 31 13 15 27 15 27 31 7 31 15 7 22 29 7 29 15 15 31 23 31 29 15 13 15 30 15 23 31 31 23 31 30 23 29 11 7 30 27 11 27 27 25 15 28 23 31 15 19 15 28 31 11 13 11 30 23 30 31 27 31 27 30 31 11 7 29 29 23 29 15 31 31 29 29 22 31 23 15 15 14 11 19 23 30 23 30 27 23 31 19 22 15 29 23 21 21 15 29 30 15 15 31 29 27 30 31 30 31 23 30 29 15 15 29 31 29 21 15 30 23 15 27 11 21 27 22 30 25 31 23 7 23 31 14 23 31 15 29 15 14 23 23 15 31 23 14 31 23 7 23 7 15 31 15 15 30 15 31 13 11 27 13 13 30 31 29 31 31 14 31 15 11 27 28 30 28 31 15 31 15 31 30 27 25 29 30 29 27 15 31 14 31 15 28 30 30 31 31 27 29 27 15 31 15 30 27 31 31 31 31 26 29 29 30 31 31 31 15 31 31 27 26 31 27 25 30 27 11 30 31 15 30 15 31 31 30 30 15 30 30 15 30 27 14 31 30 31 31 31 29 14 31 15 15 31 31 27 27 13 31 28 15 15 30 31 31 31 15 14 31 13 13 31 29 15 31 15 29 30 31 14 31 31 30 27 21 30 31 30 29 27 30 31 23 29 28 15 21 21 15 30 31 29 15 22 15 11 31 25 27 11 30 25 27 27 15 13 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30 23 23 22 31 27 15 27 15 21 28 31 7 15 15 15 23 13 31 13 25 23 31 23 31 22 30 23 30 27 31 11 15 15 31 30 15 25 14 31 22 31 27 26 29 23 31 27 15 31 23 11 23 22 31 21 15 19 29 14 31 23 23 23 7 15 29 15 23 29 30 31 27 14 31 15 23 23 28 30 23 29 31 29 19 27 29 15 23 25 29 31 15 29 15 27 14 31 30 30 31 31 7 30 30 30 31 7 29 31 29 13 29 23 29 31 23 23 15 29 31 31 7 23 31 7 31 15 31 15 26 31 15 13 29 13 30 31 31 29 31 31 13 29 30 30 31 31 15 27 30 27 31 11 28 31 28 15 27 29 27 29 15 13 15 27 15 13 29 25 15 30 31 31 31 15 15 15 31 25 30 15 15 29 30 31 29 31 31 29 27 27 25 25 31 28 27 31 14 31 31 27 31 28 31 15 13 29 27 15 30 31 15 31 15 30 31 15 15 13 11 27 15 30 31 14 31 15 29 28 29 29 31 31 15 31 15 15 31 14 31 31 31 28 31 31 31 29 31 15 31 14 31 30 18 22 30 31 26 23 22 30 25 22 29 26 31 31 19 31 28 30 23 26 31 31 27 23 25 29 23 22 27 31 31 23 27 31 30 30 31 27 29 23 21 22 27 27 22 30 23 30 31 27 26 29 26 31 29 25 23 30 27 29 29 29 27 23 27 23 31 23 23 30 31 26 30 31 29 23 28 31 19 27 23 29 23 23 26 23 27 30 23 28 27 30 27 27 23 23 30 27 23 22 31 31 30 31 23 31 22 29 29 29 23 29 29 29 31 31 23 31 23 30 30 31 31 23 21 31 29 28 23 29 27 31 27 27 31 25 25 22 27 29 23 30 29 23 30 23 19 31 27 29 25 23 21 27 27 31 23 31 23 21 29 23 23 23 31 31 23 30 23 27 27 31 22 23 25 29 27 26 31 31 27 30 31 23 21 22 23 26 31 23 27 22 31 23 31 30 27 26 27 27 31 30 31 23 23 31 28 31 27 19 31 21 31 29 25 27 31 21 31 30 31 31 31 31 31 30 31 22 23 29 21 30 29 31 31 31 23 29 23 31 29 29 29 23 22 31 30 31 31 27 29 30 29 21 23 29 29 31 23 28 31 29 23 23 29 23 29 19 23 23 23 30 31 23 29 22 31 27 19 29 25 29 28 31 31 29 31 31 21 21 23 30 31 31 31 26 23 31 27 23 19 30 31 31 28 31 22 27 19 31 29 29 21 25 29 31 31 31 31 30 31 27 26 31 23 30 30 23 29 18 31 25 31 31 30 31 31 23 23 29 29 22 31 21 27 30 30 31 19 22 23 23 31 31 31 29 23 29 23 30 31 31 30 21 30 30 30 31 31 31 23 30 22 31 26 30 29 27 31 31 28 31 29 31 21 31 29 27 31 29 23 30 31 23 31 23 26 23 23 27 27 19 31 31 23 26 30 31 25 23 25 19 27 31 27 31 26 23 25 23 19 31 30 31 31 31 23 29 29 21 31 23 27 31 27 31 31 21 29 27 29 29 23 31 27 27 31 30 19 29 26 29 30 23 31 25 30 31 27 29 27 19 23 31 31 29 30 31 31 23 31 22 23 31 23 31 30 30 23 28 31 29 29 31 23 23 31 29 29 23 23 28 31 31 22 31 31 30 31 30 27 30 27 27 30 29 29 26 29 29 27 30 29 31 29 27 28 31 29 31 27 30 31 31 29 30 30 31 31 31 26 30 31 29 31 31 30 31 31 28 31 27 30 29 31 31 27 27 31 28 30 31 27 27 31 30 31 29 25 25 26 30 31 27 30 27 30 31 29 30 27 30 31 31 26 30 31 31 31 28 31 27 25 31 31 31 27 31 31 31 31 31 25 27 27 27 31 31 27 31 28 28 30 31 30 31 29 31 29 29 31 31 29 29 31 29 28 31 31 31 30 31 30 31], [5 5 5 5 5]);\r\ntruth = reshape([1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 95 94 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 98 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 96 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 6 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 38 37 0 0 40 39 122 123], [5 5 5 5 5]);\r\nassert(isequal(solve_maze5(maze), truth));","published":true,"deleted":false,"likes_count":5,"comments_count":19,"created_by":134,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":48,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":35,"created_at":"2017-10-12T15:00:57.000Z","updated_at":"2026-03-19T20:06:56.000Z","published_at":"2017-10-16T01:51:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe traditional maze is 2-dimensional: the navigator can move in the positive or negative directions along two axes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Now imagine, if you will, a 5-dimensional maze. As in the 2-dimensional case, the navigator may only move along one of these directions at any time, and some of the directions are blocked by walls. Your task is to find and give the shortest path through the given maze.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is a generalization of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/283\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 283\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. If you haven't solved that yet, I would recommend solving it first.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEncoding\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maze will be represented by an [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eO\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e x\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eQ\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ] matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach element of the matrix represents a valid location in the maze and the value of each element is a binary-coded representation of the walls, positive directions in which you can not move. If a value reads 0, it means the navigator is permitted to move along any of the five dimensions in the positive direction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWalls are bi-directional: if a wall exists between two locations, you cannot traverse it in either direction. A skilled navigator must check the destination location's walls if she wishes to move in the negative direction along any dimension.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe start position is at the origin: subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(1,1,1,1,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe end position is at the furthest extent: subscript\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e(M,N,O,P,Q)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output should be a matrix of the same size as the input matrix that lists the steps you need to go through to traverse the maze with the remaining squares being 0. Refer to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/283\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 283\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for a 2-D example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOT\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e guaranteed that there will be only one shortest path for the test cases. If there exist multiple shortest paths, you must represent them all. It can easily be shown that the superposition of two shortest paths will never lead to a multi-valued element in the output matrix.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2646,"title":"Determine the number of maximal cliques in an undirected graph","description":"In an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A _maximal_ clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\r\n\r\nGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\r\n\r\n*Example*\r\n\r\nConsider the graph shown below,\r\n\r\n\u003c\u003chttps://dl.dropboxusercontent.com/u/13345152/examplegraph.png\u003e\u003e\r\n\r\nwhich has the following adjacency matrix:\r\n\r\n A = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\r\nThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\r\n\r\nNOTE: You may assume the data type of the adjacency matrix is double.","description_html":"\u003cp\u003eIn an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A \u003ci\u003emaximal\u003c/i\u003e clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\u003c/p\u003e\u003cp\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsider the graph shown below,\u003c/p\u003e\u003cimg src = \"https://dl.dropboxusercontent.com/u/13345152/examplegraph.png\"\u003e\u003cp\u003ewhich has the following adjacency matrix:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\u003c/pre\u003e\u003cp\u003eThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\u003c/p\u003e\u003cp\u003eNOTE: You may assume the data type of the adjacency matrix is double.\u003c/p\u003e","function_template":"function num = maximalcliques(A)\r\n num = 0;\r\nend","test_suite":"%%\r\nA = 0;\r\nassert(isequal(maximalcliques(A),1))\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = ones(N)-eye(N);\r\n assert(isequal(maximalcliques(A),1))\r\nend\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = zeros(N);\r\n assert(isequal(maximalcliques(A),N))\r\nend\r\n\r\n%%\r\nA = [ 0 0 0 0\r\n 0 0 0 1\r\n 0 0 0 0\r\n 0 1 0 0 ];\r\nassert(isequal(maximalcliques(A),3))\r\n\r\n%%\r\nA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ];\r\nassert(isequal(maximalcliques(A),3))\r\n\r\n%%\r\nA = [ 0 1 1 0 0 0\r\n 1 0 1 1 0 0\r\n 1 1 0 0 1 0\r\n 0 1 0 0 1 0\r\n 0 0 1 1 0 1\r\n 0 0 0 0 1 0 ];\r\nassert(isequal(maximalcliques(A),5))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":"2014-10-28T21:35:49.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-10-28T21:07:46.000Z","updated_at":"2014-10-28T22:05:37.000Z","published_at":"2014-10-28T21:35:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn an undirected graph, a clique is a subset of vertices that are fully connected, i.e. there is an edge between all pairs of vertices in the subset. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emaximal\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e clique is one in which the subset of vertices is not part of a larger clique. So, for instance, a fully connected graph has many cliques, but only one maximal clique.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return the number (num) of maximal cliques.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the graph shown below,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich has the following adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [ 0 1 0 0 0\\n 1 0 1 1 0\\n 0 1 0 1 0\\n 0 1 1 0 1\\n 0 0 0 1 0 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe number of maximal cliques is 3. The maximal cliques are {1,2}, {2,3,4}, and {4,5}.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: You may assume the data type of the adjacency matrix is double.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":2647,"title":"Find the maximal cliques in an undirected graph","description":"This is a variant of a \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph previous problem\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\r\n\r\nGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\r\n\r\n*Example*\r\n\r\nConsider the graph shown below,\r\n\r\n\u003c\u003chttps://dl.dropboxusercontent.com/u/13345152/examplegraph.png\u003e\u003e\r\n\r\nwhich has the following adjacency matrix:\r\n\r\n A = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\r\nThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\r\n\r\n C = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ]\r\n\r\nNOTE: You may assume the data type of the adjacency matrix (A) is double.","description_html":"\u003cp\u003eThis is a variant of a \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph\"\u003eprevious problem\u003c/a\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\u003c/p\u003e\u003cp\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsider the graph shown below,\u003c/p\u003e\u003cimg src = \"https://dl.dropboxusercontent.com/u/13345152/examplegraph.png\"\u003e\u003cp\u003ewhich has the following adjacency matrix:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ]\r\n\u003c/pre\u003e\u003cp\u003eThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eC = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ]\r\n\u003c/pre\u003e\u003cp\u003eNOTE: You may assume the data type of the adjacency matrix (A) is double.\u003c/p\u003e","function_template":"function C = maximalcliques(A)\r\n C = [];\r\nend","test_suite":"%%\r\nA = 0;\r\nassert(isequal(maximalcliques(A),1))\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = ones(N)-eye(N);\r\n assert(isequal(maximalcliques(A),ones(N,1)))\r\nend\r\n\r\n%%\r\nfor ii=1:10\r\n N = randi(100);\r\n A = zeros(N);\r\n C = maximalcliques(A);\r\n assert(isequal(fliplr(sortrows(C')'),eye(N)))\r\nend\r\n\r\n%%\r\nA = [ 0 0 0 0\r\n 0 0 0 1\r\n 0 0 0 0\r\n 0 1 0 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0\r\n 0 1 0\r\n 0 0 1\r\n 0 1 0 ];\r\nassert(isequal(C,C_correct))\r\n\r\n%%\r\nA = [ 0 1 0 0 0\r\n 1 0 1 1 0\r\n 0 1 0 1 0\r\n 0 1 1 0 1\r\n 0 0 0 1 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0\r\n 1 1 0\r\n 0 1 0\r\n 0 1 1\r\n 0 0 1 ];\r\nassert(isequal(C,C_correct))\r\n\r\n%%\r\nA = [ 0 1 1 0 0 0\r\n 1 0 1 1 0 0\r\n 1 1 0 0 1 0\r\n 0 1 0 0 1 0\r\n 0 0 1 1 0 1\r\n 0 0 0 0 1 0 ];\r\nC = maximalcliques(A);\r\nC = fliplr(sortrows(C')');\r\nC_correct = [ 1 0 0 0 0\r\n 1 1 0 0 0\r\n 1 0 1 0 0\r\n 0 1 0 1 0\r\n 0 0 1 1 1\r\n 0 0 0 0 1 ];\r\nassert(isequal(C,C_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2014-10-28T22:05:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-10-28T21:53:54.000Z","updated_at":"2014-10-28T22:05:02.000Z","published_at":"2014-10-28T22:05:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a variant of a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2646-determine-the-number-of-maximal-cliques-in-an-undirected-graph\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eprevious problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e on maximal cliques. Instead of simply computing the number of maximal cliques in an undirected graph, now you must return the cliques themselves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an NxN adjacency matrix (A) for an undirected graph with N vertices, return an array (C) in which each column encodes one of the maximal cliques in the graph. If C(i,j) = 1, then the ith vertex in the graph is included in the jth clique. The order of columns does not matter, but all maximal cliques must be given - that is, size(C,2) should equal the number of maximal cliques.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the graph shown below,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich has the following adjacency matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [ 0 1 0 0 0\\n 1 0 1 1 0\\n 0 1 0 1 0\\n 0 1 1 0 1\\n 0 0 0 1 0 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maximal cliques are {1,2}, {2,3,4}, and {4,5}. Therefore, one (of three) valid outputs is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[C = [ 1 0 0\\n 1 1 0\\n 0 1 0\\n 0 1 1\\n 0 0 1 ]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: You may assume the data type of the adjacency matrix (A) is double.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":1886,"title":"Graceful Double Wheel Graph","description":"\u003chttp://en.wikipedia.org/wiki/Graceful_labeling Graceful Graphs\u003e are the topic of the \u003chttp://www.azspcs.net/Contest/GracefulGraphs Primes Graceful Graph Contest\u003e , 21 September 2013 thru 21 December 2013.\r\n\r\nThis Challenge is to create \u003chttp://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html Graceful Double Wheel Graphs\u003e for various N. A \u003chttp://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf General Algorithm by Le Bras of Cornell\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon \u003chttp://oeis.org/A004137 OEIS A004137\u003e.\r\n\r\n*Example:*\r\nOne solution for N=11:\r\n\r\n\u003c\u003chttp://www.comp.leeds.ac.uk/bms/Graceful/Images/2C5+K1.gif\u003e\u003e\r\n\r\nwhich could be answered as [1 3 14 6 19;20 5 17 7 16].\r\n\r\nThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\r\n\r\n*Input:* N [Total number of Nodes (odd) and N\u003e10 ]\r\n\r\n*Output:* M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]","description_html":"\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Graceful_labeling\"\u003eGraceful Graphs\u003c/a\u003e are the topic of the \u003ca href = \"http://www.azspcs.net/Contest/GracefulGraphs\"\u003ePrimes Graceful Graph Contest\u003c/a\u003e , 21 September 2013 thru 21 December 2013.\u003c/p\u003e\u003cp\u003eThis Challenge is to create \u003ca href = \"http://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html\"\u003eGraceful Double Wheel Graphs\u003c/a\u003e for various N. A \u003ca href = \"http://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf\"\u003eGeneral Algorithm by Le Bras of Cornell\u003c/a\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon \u003ca href = \"http://oeis.org/A004137\"\u003eOEIS A004137\u003c/a\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\r\nOne solution for N=11:\u003c/p\u003e\u003cimg src = \"http://www.comp.leeds.ac.uk/bms/Graceful/Images/2C5+K1.gif\"\u003e\u003cp\u003ewhich could be answered as [1 3 14 6 19;20 5 17 7 16].\u003c/p\u003e\u003cp\u003eThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e N [Total number of Nodes (odd) and N\u003e10 ]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]\u003c/p\u003e","function_template":"function m=double_wheel(n)\r\n m=[];\r\nend","test_suite":"%%\r\ntic\r\nn=11;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=13;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=17;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=19;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=71;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n%%\r\nn=97;\r\nm=double_wheel(n);\r\nms=circshift(m,[0 -1]);\r\ndm=m-ms;\r\nd=unique([m(:) abs(dm(:))]);\r\nassert(all(diff(d)==1))\r\nassert(length(d)==2*(n-1))\r\nassert(max(d)==2*(n-1))\r\ntoc\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-21T23:15:03.000Z","updated_at":"2013-09-22T01:16:42.000Z","published_at":"2013-09-22T01:16:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Graceful_labeling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGraceful Graphs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e are the topic of the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.azspcs.net/Contest/GracefulGraphs\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePrimes Graceful Graph Contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e , 21 September 2013 thru 21 December 2013.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to create\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.comp.leeds.ac.uk/bms/Graceful/doublewheel.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGraceful Double Wheel Graphs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for various N. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.cs.cornell.edu/~lebras/publications/LeBras2013Double.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGeneral Algorithm by Le Bras of Cornell\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may be helpful, Section 3 for Even/Odd Rings. The Double Wheel Graph produces valid but not Maximum Edge Graceful Graph solutions based upon\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://oeis.org/A004137\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eOEIS A004137\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e One solution for N=11:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich could be answered as [1 3 14 6 19;20 5 17 7 16].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are 20 links and thus the absolute differences between connected nodes must produce values 1 thru 20. The max node value is equal to the number of links and the min is zero, at the center of the Double Wheel.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N [Total number of Nodes (odd) and N\u0026gt;10 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M [ Matrix size [(N-1)/2, 2] of node values where row-1 is outer and row-2 is inner ring ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,<!DOCTYPE html>
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Saturday 10 October 2020, 10:00 - 16:00                                            </p>

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