How do I approximate the condition number of a large matrix $G$, if $G$ is a combination of Fourier transforms $F$ (non-uniform or uniform), finite differences $R$, and diagonal matrices $S$?

The matrices are very large and not stored in memory and are only available as functions.

In particular, I have the following matrix:

$G_\mu=S^HF^HFS+\mu R^HR$

I want to investigate the relation between $\mu$ and the condition number $k(G_\mu)$.

I assume one needs some kind of iterative approach? Optimally there would be some MATLAB code availabe.

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    $\begingroup$ How about trying with the definition of the condition number and using triangle inequality and submultiplicativity? I guess you should be able to say something about norms/conditions of each of your matrices. This way you get an estimate of the condition number of $G_\mu$. $\endgroup$
    – Anke
    Apr 10, 2013 at 9:11

1 Answer 1


MATLAB has a couple of "exact" functions for this, cond and rcond, with the latter returning a reciprocal of the condition number. Matlab approximate function condest is more fully described below.

Often estimates of the condition number are generated as by-products of the solution of a linear system for the matrix, so you might be able to piggyback the condition number estimates on other work you need to do anyway. See here for a brief description of how estimates are computed. Also Sandia Labs AztecOO documentation remarks (see Sec. 3.1) that optional condition number estimates are available from iterative solvers (using the generated tridiagonal Lanczos matrix with Conjugate Gradients or the generated Hessenburg matrix with Restarted GMRES).

Since your matrices are "very large" and "only available as functions", the logical approach would be a method that piggybacks on a conjugate gradient solver or variant.

A recent arXiv.org paper Non-stationary extremal eigenvalue approximations in iterative solutions of linear systems and estimators for relative error proposes such an approach and has a few citations to the earlier literature.

Now that I look, this forum has a number of closely related previous Questions (not all with Answers, but check Comments):

Estimate extreme eigenvalues with CG

Estimation of condition numbers for very large matrices

Fastest algorithm to compute the condition number of a large matrix in Matlab/Octave

Since availability of MATLAB code was part of the Question, here's some information about condest, a built-in function that estimates the 1-norm condition number $\|A\|_1 \|A^{-1}\|_1$. The idea is from Hager(1984), with a 2010 write-up and extensions here, to explicitly compute $\|A\|_1$ (find maximum 1-norm of a column) and estimate $\|A^{-1}\|_1$ by a gradient method. See also John Burkardt's CONDITION, a MATLAB library (other languages available) "for computing or estimating the condition number of a matrix."

Since your matrix is apparently Hermitian and positive definite, perhaps the 2-norm condition number is of greater interest. The problem then amounts to estimating the ratio of largest to smallest (absolute) eigenvalues. The challenge is somewhat parallel to the 1-norm case in that generally a good estimate for the largest eigenvalue can be easily obtained, but estimating the smallest eigenvalue proves more difficult.

Although aiming at non-SPD (and even non-square) cases, this recent arXiv.org paper, Reliable Iterative Condition-Number Estimation, gives a good overview of the smallest eigenvalue estimation problem and a promising line of attack by a Krylov-subspace method (LSQR) that amounts to Conjugate Gradients in the SPD case.

  • $\begingroup$ Thank you for your answer. How do I get the condition number as a side product of a conjugate gradient solver? $\endgroup$
    – Stiefel
    Apr 10, 2013 at 12:52
  • $\begingroup$ @Stiefel: There's a 1992 paper On the approximate computation of extreme eigenvalues and the condition number of nonsingular matrices by Lei Guang-yao. Let me see if I can find a better reference (not behind a pay-wall). $\endgroup$
    – hardmath
    Apr 10, 2013 at 13:59
  • $\begingroup$ @Stiefel: Added some links. You may also be interested in checking Google Books (or a library) for Owe Axelsson's Iterative Solution Methods (1996), esp. Chap. 13 The Rate of Convergence of the Conjugate Gradient Method, though the emphasis there is on getting better estimates of number of iterations required for convergence than the condition number alone provides. $\endgroup$
    – hardmath
    Apr 10, 2013 at 14:43
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    $\begingroup$ @Stiefel With a name like yours you should be teaching us about the CG method :) See en.wikipedia.org/wiki/Eduard_Stiefel $\endgroup$
    – stali
    Apr 11, 2013 at 16:11

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