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Equivalent of LSQR in matlab


From: Jean-Baptiste Poullet
Subject: Equivalent of LSQR in matlab
Date: Thu, 13 Sep 2007 09:12:39 +0200

Thanks for your answers! I'm also wondering whether the matlab function
LSQR (based on Conjugate Gradients on the Normal Equations) exists in
octave. I'm dealing with the least squares problem Ax=b, A being a MxN
Vandermonde matrix (complex values), x and b being vectors. Is there any
function in Octave for solving this problem, taking into account the
special structure of A (x=A\b is not sufficient given A is large (M>>N)
and ill conditioned)? 

Thx.

JB



On Tue, 2007-09-11 at 22:54 +0200, David Bateman wrote:
> John W. Eaton wrote:
> > On 11-Sep-2007, David Bateman wrote:
> > 
> > | David Bateman wrote:
> > | > 
> > | > I've often felt that Octave should change to use the same solver as
> > | > Matlab, especially now as the sparse matrices in Octave use a QR solver
> > | > with pivoting and don't give exactly the same results as the dense 
> > solvers..
> > | 
> > | If you want another reason to change consider Figure 3.3 on the page
> > | 
> > | http://www.netlib.org/lapack/lug/node71.html
> > | 
> > | for N = 500 dgelss is about 35 times slower than dgelsy
> > 
> > Switching is fine with me if the results are approximately as good.  I
> > chose dgelss because it was available and I knew about it.
> 
> If we want to stay with an SVD solver we should at least change to
> dgelsd that is significantly faster than dgelss and gives essentially
> the same results.
> 
> D.
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