[SciPy-dev] Why does orth use svd instead of QR ?
Charles R Harris
Fri Feb 5 01:47:09 CST 2010
On Fri, Feb 5, 2010 at 12:18 AM, David Cournapeau <email@example.com>wrote:
> Charles R Harris wrote:
> > On Thu, Feb 4, 2010 at 8:45 PM, David Cournapeau <firstname.lastname@example.org
> > <mailto:email@example.com>> wrote:
> > Hi,
> > I wanted to know if there was a rationale for using svd to
> > orthonormalize the columns of a matrix (in scipy.linalg). QR-based
> > methods are likely to be much faster, and I thought this was the
> > standard, numerically-stable method to orthonormalize a basis ? If
> > reason is to deal with rank-deficient matrices, maybe we could add an
> > option to choose between them ?
> > QR with column rotation would deal with rank-deficient matrices and
> > routines for that are available in LAPACK
> > <http://netlib.org/lapack/lug/node42.html>. The SVD was probably used
> > because it was available. The diagonal elements of the R matrix can
> > somewhat take the place of the singular values when column rotation is
> So would be it ok to use this column-rotated QR in place of svd for
> every case in orth ? I would have to check that QR with column rotation
> is still significantly faster than svd, but I would surprised if if were
> not the case. QR has also the advantage of being implemented in PLASMA
> already contrary to eigen/svd solvers,
I don't know how the two methods compare in practice. SVD algorithms
generally use iterated QR reductions in their implementation, so QR
reductions can't be worse numerically. But the SVD probably provides a
better metric for rank determination. A google search turns up some
literature on the subject that I can't access from home.
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