[petsc-users] SLEPc Bogus eigenvalues for large -eps_nev
Vijay Gopal Chilkuri
vijay.gopal.c at gmail.com
Thu May 14 10:28:52 CDT 2015
I get the proper eigenvalues (and quite fast) when I ask for only 10-15 of
the lowest eigenvalues.
The only problem is when I ask for m ~ 100-300 of the lowest eigenvalues.
Is it possible that asking for large number of eigenvalues would cause
problems ?
I've got the same problem with smaller matrices of size 540540. Again for
this system, if I ask for
10-15 eigenvalues I get the proper correct converged eigenvalues, but when
I ask for more than 100 eigevalues
I get incorrect results !
If you would like to test I can send you the proper files.
Thanks,
Vijay
On Thu, May 14, 2015 at 5:17 PM, Jose E. Roman <jroman at dsic.upv.es> wrote:
>
> On 14/05/2015, Vijay Gopal Chilkuri wrote:
>
> > Hi,
> >
> > I am using SLEPc to get the lowest m eigenvalues of a large sparse
> hermitian matrix using Krylov-Schur.
> > The problem is that while asking for a large number of eigenvalues (e.g.
> 300) for a large problem
> > consisting of a 91454220 X 91454220 system, the solver does not give the
> lowest 300 eigenvalues.
> > Instead it gives the following arbitrary negative values followed by a
> few correct ones.
> >
> > -4733.882429 0.999888
> > -4729.554413 1.0006
> > -4725.841715 0.999887
> > -4702.457861 1.00061
> > -4700.608105 0.999887
> > -4691.142733 0.999885
> > -6.361168 1.18973
> > -6.361102 6.05898e-09
> > -6.361099 6.71e-09
> > -6.361093 7.66471e-09
> > -6.361082 1.17659
> >
> >
> > Although the solver says that it has successfully converged the 300
> eigenvalues !
> > Please find the relevant files attached.
> >
> > Could anyone tell me what I'm doing wrong ?
> >
> > thanks,
> > Vijay
> >
> > <problem.c><output>
>
> I do not see anything strange, apart from passing a pointer to long int in
> an argument of type PetscInt*. To guarantee portability, it would be better
> if all variables in your program are of type PetscInt, PetscMPIIInt,
> PetscReal, PetscScalar, etc.
>
> I do not know what is going on. Do you get the same behaviour for smaller
> matrix sizes? Do you get the same problem when computing only a few
> eigenvalues?
>
> Jose
>
>
>
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