Oops, the second line should have been
export OPENBLAS_NUM_THREADS=8
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> >/ I get a segmentation fault upon running the following:
> />/
> />/ import numpy
> />/ A = numpy.ones((700, 8))
> />/ Q, R = numpy.linalg.qr(A)
> />/
> />/ on Python 2.7.3, Linux 64-bit using numpy 1.9.0.dev-ec3603f linked
> />/ against OpenBLAS. If A is a smaller matrix then the QR decompo
I get a segmentation fault upon running the following:
import numpy
A = numpy.ones((700, 8))
Q, R = numpy.linalg.qr(A)
on Python 2.7.3, Linux 64-bit using numpy 1.9.0.dev-ec3603f linked
against OpenBLAS. If A is a smaller matrix then the QR decomposition
works (for example A has shape (40
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 abou
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 ma
Thanks very much Lou for the information. I tried delving into the C
code and found a line in the dlasd4_ routine which reads:
for (niter = iter; niter <= MAXITERLOOPS; ++niter) {
This is apparently the main loop for this subroutine and the value of
MAXITERLOOPS = 100. All I did was increase th
I had a quick look at the code
(https://github.com/numpy/numpy/blob/master/numpy/linalg/linalg.py) and
the numpy.linalg.svd function calls lapack_lite.dgesdd (for real
matrices) so I guess the non-convergence occurs in this function. As I
understood lapack_lite is used by default unless numpy i
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 De
