how to get all the singular values of the matrix?

[Yujie]

Dear Matt and Hong:

Based what you said, it looks like a little difficult to evalute the matrix
in PETSc, especailly regarding a big dimension. However, when I select
iterative methods, how to select a suitable one based on some evaluation?
Could you give me some advice? thanks a lot.

Regards,
Yujie

On 1/14/08, Hong Zhang <hzhang at mcs.anl.gov> wrote:
>
>
> If you want few selected eigen solutions of sparse matrix,
> you should use sparse eigen solver. Take a look at'
> slepc (http://www.grycap.upv.es/slepc/)
> or use slepc interface with arpack.
>
> Hong
>
>
> On Mon, 14 Jan 2008, Yujie wrote:
>
> > Thank you for your advice.
> > I have used -ksp_compute_eigenvalues_explicitly to get the eigen values.
> > However, it is very very
> > slow because the dimension of the matrix is about ten thousand.
> >
> > Yujie
> >
> > On 1/14/08, Matthew Knepley <knepley at gmail.com> wrote:
> >>
> >> You can use
> >>
> >>
> >>
> http://www-unix.mcs.anl.gov/petsc/petsc-as/snapshots/petsc-current/docs/manualpages/KSP/KSPComputeEigenvaluesExplicitly.html
> >>
> >> with and without a preconditioner. We have not coded the SVD
> >> counterpart, but you can use
> >>
> >>
> >>
> http://www-unix.mcs.anl.gov/petsc/petsc-as/snapshots/petsc-current/docs/manualpages/KSP/KSPComputeExplicitOperator.html
> >>
> >> and then call the LAPACK yourself.
> >>
> >>   Matt
> >>
> >> On Jan 13, 2008 11:23 PM, Yujie <recrusader at gmail.com> wrote:
> >>> Hi, everyone
> >>>
> >>> I want to select iterative methods by observing the singular values
> >>> decompostion of the matrix. However, I don't know how to get all the
> >>> singular values of the matrix in PETSc. I know the command
> >>> "-ksp_monitor_singular_value" may get the max and min singular values
> at
> >>> each iteration. How to get the singular values of the matrix I want to
> >>> solve? In addition, when I use the preconditioned iterative method,
> how
> >> to
> >>> get the singular values of the preconditioned iterative operator?
> >>>
> >>> thanks a lot.
> >>>
> >>> Regards,
> >>> Yujie
> >>>
> >>
> >>
> >>
> >> --
> >> What most experimenters take for granted before they begin their
> >> experiments is infinitely more interesting than any results to which
> >> their experiments lead.
> >> -- Norbert Wiener
> >>
> >>
> >
>
>
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