#11248: SVD matrix decomposition may return a non-invertible "unitary" matrix
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Reporter: rbeezer | Owner: jason, was
Type: defect | Status: new
Priority: major | Milestone: sage-4.7
Component: linear algebra | Keywords:
Work_issues: | Upstream: N/A
Reviewer: | Author:
Merged: | Dependencies:
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Comment(by rbeezer):
Hi Samuel,
Pure numpy/scipy with the same matrix causing the doctest failure. Start
Sage with
{{{
sage -python
}}}
which should give you a {{{>>>}}} prompt. Paste in the code block below.
If it goes as expected, I'd expect now a final row of zeros in the
{{{VH}}} matrix. If so, I'll report it to the numpy discussion list, I
guess. If not, then Sage's manipulations of the results may be at fault.
Thanks,
Rob
{{{
import numpy
import scipy.linalg
A = numpy.array( [[1 - 1j, -3j, -2 + 1j, 1, -2 + 3j],
[ 1 - 1j, -2 + 1j, 1 + 4j, 0, 2 + 1j],
[ -1, -5 + 1j, -2 + 1j, 1 + 1j, -5 - 4j],
[-2 + 4j, 2 - 1j, 8 - 4j, 1 - 8j, 3 - 2j]])
U, S, VH = scipy.linalg.svd(A)
VH
numpy.dot(VH, VH.conj().T)
}}}
--
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/11248#comment:3>
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