[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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