```echo on

n = 100;
A = randn(2*n,n);
b = randn(2*n,1);
cvx_begin
variable x(n)
minimize( norm( A*x-b ) )
cvx_end

echo off
```
```n = 100;
A = randn(2*n,n);
b = randn(2*n,1);
cvx_begin
variable x(n)
minimize( norm( A*x-b ) )
cvx_end

Calling sedumi: 201 variables, 101 equality constraints
For improved efficiency, sedumi is solving the dual problem.
------------------------------------------------------------
SeDuMi 1.21 by AdvOL, 2005-2008 and Jos F. Sturm, 1998-2003.
Alg = 2: xz-corrector, Adaptive Step-Differentiation, theta = 0.250, beta = 0.500
eqs m = 101, order n = 3, dim = 202, blocks = 2
nnz(A) = 20001 + 0, nnz(ADA) = 10201, nnz(L) = 5151
it :     b*y       gap    delta  rate   t/tP*  t/tD*   feas cg cg  prec
0 :            6.88E+00 0.000
1 :  -8.51E+00 1.66E+00 0.000 0.2408 0.9000 0.9000  -0.52  1  1  2.1E+00
2 :  -1.10E+01 1.62E-01 0.000 0.0977 0.9900 0.9900   0.97  1  1  2.4E-01
3 :  -1.08E+01 9.64E-05 0.387 0.0006 0.9999 0.9999   1.04  1  1  1.4E-04
4 :  -1.08E+01 5.79E-07 0.000 0.0060 0.9990 0.9990   1.62  1  1  6.7E-07
5 :  -1.08E+01 7.28E-14 0.000 0.0000 1.0000 1.0000   1.00  1  1  8.9E-14

iter seconds digits       c*x               b*y
5      0.0   Inf -1.0766202444e+01 -1.0766202444e+01
|Ax-b| =   4.3e-14, [Ay-c]_+ =   1.7E-13, |x|=  1.4e+00, |y|=  1.1e+01

Detailed timing (sec)
Pre          IPM          Post
1.000E-02    4.000E-02    0.000E+00
Max-norms: ||b||=1, ||c|| = 3.449504e+00,
Cholesky |add|=0, |skip| = 0, ||L.L|| = 1.
------------------------------------------------------------
Status: Solved
Optimal value (cvx_optval): +10.7662

echo off
```