Yes, as I pointed out, the problem does run on the Macintosh systems. But I'd like to be able to run these on our linux supercomputers. Surely this is possible, right? On Mon, Aug 29, 2011 at 9:31 AM, Paul Anton Letnes < paul.anton.letnes@gmail.com> wrote:

I recently got into trouble with these calculations (although I used scipy). I actually got segfaults and "bus errors". The solution for me was to not link against ATLAS, but rather link against Apple's blas/lapack libraries. That got everything working again. I would suggest trying to install against something other than ATLAS and see if that helps (or, more generally, determining which blas/lapack you are linking against, and try something else).

Paul

On 29. aug. 2011, at 16.21, Charanpal Dhanjal wrote:

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.

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