Q-R decomposition with positive diagonals of R Matrix
In linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of a matrix A into a product A = QR of an orthogonal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigen value algorithm, the QR algorithm. If A has n linearly independent columns, then the first n columns of Q form an orthonormal basis for the column space of A. More specifically, the first k columns of Q form an orthonormal basis for the span of the first k columns of A for any 1 ≤ k ≤ n. The fact that any column k of A only depends on the first k columns of Q is responsible for the triangular form of R.
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Gnaneswar Nadh satapathi (2024). Q-R decomposition with positive diagonals of R Matrix (https://www.mathworks.com/matlabcentral/fileexchange/49807-q-r-decomposition-with-positive-diagonals-of-r-matrix), MATLAB Central File Exchange. Récupéré le .
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Version | Publié le | Notes de version | |
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1.2.0.0 | Q-R decomposition for random matrix with positive diagonal elements |
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1.1.0.0 | Positive diagonals of R matrix for a random input matrix |
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1.0.0.0 |