[Numpy-discussion] Eigenvalues did not converge

Charanpal Dhanjal dhanjal@telecom-paristech...
Mon Aug 29 10:21:05 CDT 2011

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?


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.

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