You cannot compute all the eigenvalues for a large sparse matrix with PETSc alone.
Barry On Jan 14, 2008, at 11:24 AM, Yujie wrote: 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@xxxxxxxxxxx> 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@xxxxxxxxx> 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@xxxxxxxxx> 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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