I posted a similar question about the non-convergence of numpy.linalg.svd a few weeks ago. I'm not sure I can help but I wonder if you compiled numpy with ATLAS/MKL support (try numpy.show_config()) and whether it made a difference? Also what is the condition number and Frobenius norm of the matrix in question? Charanpal On Mon, 29 Aug 2011 08:56:31 -0600, Rick Muller wrote:

Im bumping into the old "Eigenvalues did not converge" error using numpy.linalg.eigh() on several different linux builds of numpy (1.4.1). The matrix is 166x166. I can compute the eigenvalues on a Macintosh build of numpy, and I can confirm that there arent degenerate eigenvalues, and that the matrix appears to be negative definite.

Ive seen this before (though not for several years), and what I normally do is to build lapack with -O0. This trick did not work in the current instance. Does anyone have any tricks to getting eigh to work?

Other weird things that Ive noticed about this case: I can compute the eigenvalues using eigvals and eigvalsh, and can compute the eigenvals/vecs using eig(). The matrix is real symmetric, and Ive tested that its symmetric enough by forcibly symmetrizing it.

Thanks in advance for any help you can offer.