[Numpy-discussion] SVD does not converge on "clean" matrix
Fri Aug 12 04:03:30 CDT 2011
Thank Nadav for testing out the matrix. I wonder if you had a chance to
check if the resulting decomposition contained NaN or Inf values?
As far I understood, numpy.linalg.svd uses routines in LAPACK and ATLAS
(if available) to compute the corresponding SVD. I did some
complementary tests on Debian Squeeze on an Intel Xeon W3550 CPU and the
call to numpy.linalg.svd results in the LinAlgError "SVD did not
converge", however the test leading to results containing NaN values ran
on Debian Lenny on an Intel Core 2 Quad. In both of these situations we
use Python 2.7.1 and numpy 1.5.1 (without ATLAS), and so the reasons for
the differences seem to be OS or processor dependent. Any ideas?
Date: Thu, 11 Aug 2011 07:21:09 -0700
From: Nadav Horesh <firstname.lastname@example.org>
Subject: Re: [Numpy-discussion] SVD does not converge on "clean"
To: Discussion of Numerical Python <email@example.com>
Content-Type: text/plain; charset="us-ascii"
> Had no problem on a gentoo 64 bit machine using atlas 3.8.0 (Core I7,
> python 2.7.2, numpy versions1.60 and 1.6.1)
>On Thu, 11 Aug 2011 15:23:22 +0200, firstname.lastname@example.org
>> Hi all,
>> I get an error message "numpy.linalg.linalg.LinAlgError: SVD did not
>> converge" when calling numpy.linalg.svd on a "clean" matrix of size
>> 895). The matrix is clean in the sense that it contains no NaN or
>> values. The corresponding npz file is available here:
>> Here is some information about my setup: I use Python 2.7.1 on
>> 11.04 with numpy 1.6.1. Furthermore, I thought the problem might be
>> by recompiling numpy with my local ATLAS library (version 3.8.3),
>> and this
>> didn't seem to help. On another machine with Python 2.7.1 and numpy
>> the SVD does converge however it contains 1 NaN singular value and 3
>> negative singular values of the order -10^-1 (singular values should
>> always be non-negative).
>> I also tried computing the SVD of the matrix using Octave 3.2.4 and
>> 184.108.40.2069 (R2010a) 64-bit (glnxa64) and there were no problems. Any
>> is greatly appreciated.
>> Thanks in advance,
More information about the NumPy-Discussion