[Numpy-discussion] SVD does not converge
Charles R Harris
Tue Jun 28 12:43:21 CDT 2011
On Tue, Jun 28, 2011 at 11:36 AM, eat <firstname.lastname@example.org> wrote:
> On Tue, Jun 28, 2011 at 7:43 PM, Lou Pecora <email@example.com>wrote:
>> *From:* santhu kumar <firstname.lastname@example.org>
>> *To:* email@example.com
>> *Sent:* Tue, June 28, 2011 11:56:48 AM
>> *Subject:* [Numpy-discussion] SVD does not converge
>> 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?
>> 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
Yes, or similarly, start with a QR decomposition and work with R. In fact,
the SVD algorithm we use first does QR, then reduces R to tridiagonal form
(IIRC), and proceeds from there.
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