1-norm condition number estimate
c = condest(A)
c = condest(A,t)
[c,v] = condest(A)
c = condest(A,t) changes t, a positive integer parameter equal to the number of columns in an underlying iteration matrix. Increasing the number of columns usually gives a better condition estimate but increases the cost. The default is t = 2, which almost always gives an estimate correct to within a factor 2.
Note: condest invokes rand. If repeatable results are required then use rng to set the random number generator to its startup settings before using condest.
This function is particularly useful for sparse matrices.
condest is based on the 1-norm condition estimator of Hager  and a block-oriented generalization of Hager's estimator given by Higham and Tisseur . The heart of the algorithm involves an iterative search to estimate without computing A−1. This is posed as the convex but nondifferentiable optimization problem subject to
 William W. Hager, "Condition Estimates," SIAM J. Sci. Stat. Comput. 5, 1984, 311-316, 1984.
 Nicholas J. Higham and Françoise Tisseur, "A Block Algorithm for Matrix 1-Norm Estimation with an Application to 1-Norm Pseudospectra, "SIAM J. Matrix Anal. Appl., Vol. 21, 1185-1201, 2000.