1-norm condition number estimate
c = condest(A)
c = condest(A,t)
[c,v] = condest(A)
c = condest(A) computes
a lower bound
c for the 1-norm condition number
of a square matrix
c = condest(A,t) changes
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
t = 2, which almost always gives an estimate
correct to within a factor 2.
[c,v] = condest(A) also
computes a vector
v which is an approximate null
c is large.
If repeatable results are required then use
set the random number generator to its startup settings before using
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.