[Numpy-discussion] SVD error in Numpy. Bug?
Lou Pecora
lou_boog2000@yahoo....
Wed Mar 19 08:28:03 CDT 2008
I tried sending this message yesterday, but it is
being held up because the MatrixMarket attachment is
too large. The moderator my release it to the group,
but I don't know so I am sending the original message
minus the attachment. If anyone wants the
MatrixMarket version of my problem matrix, just let me
know and I will send it them directly on email.
---- The original message:
The determinant of my matrix is
Det= (1.00677345434e-24+9.56072162013e-25j)
I expect it to be small near a solution to my problem
whose solution is the vector closest to the null space
of the original matrix. That's the reason I am using
SVD.
The MatrixMarket file of the complex 36 x 36 matrix is
attached as requested.
FYI: I found a curious workaround. If I catch the
linalg.linalg.LinAlgError exception that svd throws
and then "square" the original matrix:
newmat=dot(conj(oldmat.T),oldmat)
the SVD on newmat works fine and the square root of
the minimum singular value (which is what I am looking
for) appears correct. If condition number were the
problem in some way, I would expect newmat to be
worse. Maybe the newmat symmetric form is better
behaved. Why? Beats me.
Thanks for your help.
-- Lou Pecora, my views are my own.
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