Hi, On Tue, Jun 28, 2011 at 7:43 PM, Lou Pecora <lou_boog2...@yahoo.com> wrote:
> > ------------------------------ > *From:* santhu kumar <mesan...@gmail.com> > *To:* numpy-discussion@scipy.org > *Sent:* Tue, June 28, 2011 11:56:48 AM > *Subject:* [Numpy-discussion] SVD does not converge > > Hello, > > I have a 380X5 matrix and when I am calculating pseudo-inverse of the > matrix using pinv(numpy.linalg) I get the following error message: > > raise LinAlgError, 'SVD did not converge' > numpy.linalg.linalg.LinAlgError: SVD did not converge > > I have looked in the list that it is a recurring issue but I was unable to > find any solution. Can somebody please guide me on how to fix that issue? > > Thanks > Santhosh > ============================================== > > I had a similar problem (although I wasn't looking for the pseudo inverse). > I found that "squaring" the matrix fixed the problem. But I'm guessing in > your situation that would mean a 380 x 380 matrix (I hope I'm thinking about > your case correctly). But it's worth trying since it's easy to do. > With my rusty linear algebra: if one chooses to proceed with this 'squaring' avenue, wouldn't it then be more economical to base the calculations on a square 5x5 matrix? Something like: A_pinv= dot(A, pinv(dot(A.T, A))).T Instead of a 380x380 based matrix: A_pinv= dot(pinv(dot(A, A.T)), A).T My two cents - eat > > -- Lou Pecora, my views are my own. > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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