[Numpy-discussion] Eigenvalues did not converge
Paul Anton Letnes
Sun Sep 4 09:17:07 CDT 2011
I'm not sure if I got my point across. My point was that the ATLAS
installation that numpy linked against was broken on Mac OS X but not on
Linux (afaik). Hence, your code may run better on your supercomputer. So,
try linking against a different BLAS/LAPACK implementation, and, with some
luck, your problem could potentially disappear.
On Thu, Sep 1, 2011 at 8:20 PM, Rick Muller <email@example.com> wrote:
> 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 <
> firstname.lastname@example.org> 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).
>> 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.
>> > _______________________________________________
>> > NumPy-Discussion mailing list
>> > NumPy-Discussion@scipy.org
>> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>> NumPy-Discussion mailing list
> Rick Muller
> NumPy-Discussion mailing list
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion