[petsc-users] Is PCMG a generic PC object ?

[Matthew Knepley]

I will also note that a good intro for implementing your own might be the ML
PC
in Petsc. It puts the ML AMG package into the PCMG framework.

   Matt

On Fri, Dec 3, 2010 at 3:44 AM, Dave May <dave.mayhem23 at gmail.com> wrote:

> Hey Vijay,
>  PCMG is generic. If you provide the operators for each level, along
> with the restriction and prolongation,
> you can use PCMG. It doesn't need to know about the mesh.
>
> You don't actually need to provide the coarse grid operators.
> Given the fine grid operator and R and optionally P, you can use
> Galerkin coarsening by calling
> PCMGSetGalerkin() or via the command line arg -pc_mg_galerkin
> Also, if you don't specify the prolongation, petsc will use P = R^T.
>
>
> Cheers,
>   Dave
>
>
> On 3 December 2010 06:02, Vijay S. Mahadevan <vijay.m at gmail.com> wrote:
> > Hi all,
> >
> > I was wondering whether the MG preconditioner object is generic enough
> > to work out of the box like say ILU or SOR.  To elaborate on this, if
> > I can provide the number of levels, restriction and prolongation
> > operators for each level and the system operators along with vectors
> > allocated for solution and rhs, would it work as a preconditioner for
> > my given problem and a prescribed rhs at the finest level of PCMG. Or
> > does it need some knowledge of the fine and coarser meshes to perform
> > the MG operations correctly ?
> >
> > All the examples I've seen using MG in petsc involve the DA and DMMG
> > objects and since I use my own mesh and corresponding discretization
> > code for an elliptic system, I'm curious about this usage. It would
> > not be terribly difficult to write my own framework to do a simple
> > V-cycle with my existing framework but since petsc already provides
> > this functionality along with different types of MG solves (with
> > verified code!), I really want to use it for my system. Any help
> > and/or pointers are welcome.
> >
> > Thanks,
> > vijay
> >
>



-- 
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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