[SciPy-user] Least squares for sparse matrices?
Fri Nov 21 12:14:27 CST 2008
I am trying to understand the behavior of matrices that approximate
Radon transforms and their inverses. So far, I've used commands like
the following to experiment with various values of SVDcond:
V_1,resids,rank,s = numpy.linalg.lstsq(M_Radon,U_0,rcond=SVDcond)
Is there a sparse version of lstsq that will let me exploit the
sparseness of M_Radon?
It would be even better if there were something like a sparse SVD that
would let me pre-calculate an SVD decomposition and then later use it to
invert several U_0 vectors.
Andy Fraser ISR-2 (MS:B244)
firstname.lastname@example.org Los Alamos National Laboratory
505 665 9448 Los Alamos, NM 87545
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