[petsc-users] Matrix-free generalised eigenvalue problem
Quentin Chevalier
quentin.chevalier at polytechnique.edu
Tue Jul 18 00:48:51 CDT 2023
Thank you for that pointer ! I have on hand a partial SVD of R, so I used
that to build the approximate matrix instead.
It's great that so many nice features of PETSc like STSetPreconditionerMat
are accessible through petsc4py !
Good day,
Quentin
On Mon, 17 Jul 2023 at 17:29, Jose E. Roman <jroman at dsic.upv.es> wrote:
>
> It is possible to pass a different matrix to build the preconditioner.
That is, the shell matrix for B (EPSSetOperators) and an explicit matrix
(that approximates B) for the preconditioner. For instance, you can try
passing M for building the preconditioner. Since M is an explicit matrix,
you can try the default preconditioner (block Jacobi with ILU as local
solver) or even a full LU decomposition. The effectiveness of the
preconditioner will depend on how the update M+R^H P M P R moves the
eigenvalues around.
>
> You can do this with STSetSplitPreconditioner() or
STSetPreconditionerMat(). In your case any of them will do.
>
> Jose
>
>
> > El 17 jul 2023, a las 15:50, Quentin Chevalier <
quentin.chevalier at polytechnique.edu> escribió:
> >
> > Thank you for this suggestion, I tried to implement that but it's
> > proven pretty hard to implement MATOP_GET_DIAGONAL without completely
> > tanking performance. After all, B is a shell matrix for a reason : it
> > looks like M+R^H P M P R with R itself a shell matrix.
> >
> > Allow me to point out that I have no shift. My eigenvalue problem is
> > purely about the largest ones out there. Section 8.2 and 3.4.3 led me
> > to think that there was a way to avoid computing (or writing a shell
> > matrix about it) B^-1... But you seem to stress that there's no way
> > around it.
> >
> > Quentin
> >
> >
> >
> > On Mon, 17 Jul 2023 at 11:56, Jose E. Roman <jroman at dsic.upv.es> wrote:
> >>
> >> The B-inner product is independent of the ST operator. See Table 3.2.
In generalized eigenproblems you always have an inverse.
> >>
> >> If your matrix is diagonally dominant, try implementing the
MATOP_GET_DIAGONAL operation and using PCJACOBI. Apart from this, you have
to build your own preconditioner.
> >>
> >> Jose
> >>
> >>
> >>> El 17 jul 2023, a las 11:48, Quentin Chevalier <
quentin.chevalier at polytechnique.edu> escribió:
> >>>
> >>> Hello Jose,
> >>>
> >>> I guess I expected B to not be inverted but instead used as a mass
for a problem-specific inner product since I specified GHEP as a problem
type. p50 of the same user manual seems to imply that that would indeed be
the case. I don't see what problem there would be with using a shell B
matrix as a weighting matrix, as long as a mat utility is provided of
course.
> >>>
> >>> I tried the first approach - I set up my KSP as CG since B is
hermitian positive-definite (I made a mistake in my first email), but I'm
getting a KSPSolve has not converged, reason DIVERGED_ITS error. I'm
letting it run for 1000 iterations already so it seems suspiciously slow
for a CG solver.
> >>>
> >>> I'm grappling with a shell preconditioner now to try and speed it up,
but I'm unsure which one allows for shell matrices.
> >>>
> >>> Thank you for your time,
> >>>
> >>> Quentin
> >>>
> >>>
> >>> On Wed, 12 Jul 2023 at 19:24, Jose E. Roman <jroman at dsic.upv.es>
wrote:
> >>>>
> >>>> By default, it is solving the problem as B^{-1}*A*x=lambda*x (see
chapter on Spectral Transformation). That is why A can be a shell matrix
without problem. But B needs to be an explicit matrix in order to compute
an LU factorization. If B is also a shell matrix then you should set an
iterative solver for the associated KSP (see examples in the chapter).
> >>>>
> >>>> An alternative is to create a shell matrix M that computes the
action of B^{-1}*A, then pass M to the EPS solver as a standard
eigenproblem.
> >>>>
> >>>> Jose
> >>>>
> >>>>
> >>>>> El 12 jul 2023, a las 19:04, Quentin Chevalier <
quentin.chevalier at polytechnique.edu> escribió:
> >>>>>
> >>>>> Hello PETSc Users,
> >>>>>
> >>>>> I have a generalised eigenvalue problem : Ax= lambda Bx
> >>>>> I used to have only A as a matrix-free method, I used mumps and an
LU preconditioner, everything worked fine.
> >>>>>
> >>>>> Now B is matrix-free as well, and my solver is returning an error :
"MatSolverType mumps does not support matrix type python", which is ironic
given it seem to handle A quite fine.
> >>>>>
> >>>>> I have read in the user manual here that there some methods may
require additional methods to be supplied for B like MATOP_GET_DIAGONAL but
it's unclear to me exactly what I should be implementing and what is the
best solver for my case.
> >>>>>
> >>>>> A is hermitian, B is hermitian positive but not positive-definite
or real. Therefore I have specified a GHEP problem type to the EPS object.
> >>>>>
> >>>>> I use PETSc in complex mode through the petsc4py bridge.
> >>>>>
> >>>>> Any help on how to get EPS to work for a generalised matrix-free
case would be welcome. Performance is not a key issue here - I have a
tractable high value case on hand.
> >>>>>
> >>>>> Thank you for your time,
> >>>>>
> >>>>> Quentin
> >>>>
> >>
>
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