Question regarding PCMG

[Matthew Knepley]

On 8/13/07, TsuyoshiKoyama(berkeley) <tkoyama at berkeley.edu> wrote:
> Dear petsc-users,
>
> I am currently trying to use the PCMG object and
> had some questions with the preconditioner.
>
> ---------------------------------------------------------
> 1. In this multigrid preconditioner, the default KSP smoother seems
> to be GMRES. If one wanted to use the Gauss-Seidel
> iteration as a smoother what would one do?
>
> The solution that I thought feasible is to use the PCGauss-Seidel
> and set this into the KSP smoother, and set the Krylov solver
> type to KSPPreonly. Since we are using a Gauss-Seidel smoothing
> and in the case that we include post-smoothing, the smoother must
> be able to incorporate a nonzero initial guess. Thus one would think
> of setting the flag for KSPSetInitialGuessNonzero. Unfortunately the
> combination of,
>
> -KSPPreonly
> -KSPSetInitialGuessNonzero
>
> is rejected in /src/ksp/ksp/impls/preonly/preonly.c line:24. Here it
> advises one to use a Richardson but that is not what I would like.

Actually, I believe Richardson is exactly what you want. It is just the simple
update.

> 2. In the setting of the coarse grid KSP smoother(solver) in PCMG, in the
> function
> PCSetUp_MG in file /src/ksp/pc/impls/mg/mg.c line:474 there is
> a setting that requires one to only be able to use lu, redundant, or
> cholesky as the solver in the case of preonly. This means that one would
> not again be able to use the type of solver that I have stated above,
> a Gauss-Seidel solver or any other user-specified stationary
> solver with KSPPreonly.
> ---------------------------------------------------------

I do not really understand what you want here. Can you give me more explanation?
The coarse problem by default is solved exactly.

  Thanks,

    Matt

> If you have any comments or tricks to get by this, it would be very
> helpful.
>
> Sincerely,
>
> -Tsuyoshi Koyama
-- 
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