[petsc-users] MatMatMult involving MPIAIJ and MATNEST

[Patrick Sanan]

(forgot to reply-all)
I think that depends on how sparse M^{-1} and H are.



On Mon, May 11, 2015 at 7:41 PM, Bikash Kanungo <bikash at umich.edu> wrote:

> Hi Patrick,
>
> I have M^{-1} computed explicitly and stored as MATNEST. Providing
> MATCOMPOSITE of M^{-1}H to SLEPc standard eigenvalue solvers seems an
> option. Does it has any additional cost over explicitly building M^{-1}H
> (assuming I have a way to do so) and then providing it to standard
> eigenvalue solvers?
>
> Thanks,
>
> Bikash
>
> On Mon, May 11, 2015 at 1:37 PM, Bikash Kanungo <bikash at umich.edu> wrote:
>
>> Hi Patrick,
>>
>> I have M^{-1} computed explicitly and stored as MATNEST. Providing
>> MATCOMPOSITE of M^{-1}H to SLEPc standard eigenvalue solvers seems an
>> option. Does it has any additional cost of explicitly building
>> On May 11, 2015 1:30 PM, "Patrick Sanan" <patrick.sanan at gmail.com> wrote:
>>
>>> Are you storing M^{-1} explicitly? In that case you could use
>>> MATCOMPOSITE to define M^{-1}H and feed that to SLEPc's standard solver. If
>>> you only have M explicitly, you could use MATSHELL to define your own
>>> operator which applies M^{-1}H and use that with SLEPc's standard solver
>>> (though of course SLEPc would be free to implement its generalized
>>> eigensolvers this way and/or in better ways for certain problems - I am not
>>> enough of an expert on that library to say more).
>>>
>>> On Mon, May 11, 2015 at 7:12 PM, Bikash Kanungo <bikash at umich.edu>
>>> wrote:
>>>
>>>> Hi Matthew,
>>>>
>>>> Yes SLEPc does allow me to solve a generalized eigenvalue problem. But
>>>> the generalized solvers are not as robust as the standard eigenvalue
>>>> solvers. That's the reason I want to use explicit MatMatMult so that I can
>>>> use standard eigenvalue solvers.
>>>>
>>>> Thanks,
>>>> Bikash
>>>> On May 11, 2015 1:00 PM, "Matthew Knepley" <knepley at gmail.com> wrote:
>>>>
>>>>> On Mon, May 11, 2015 at 8:47 AM, Bikash Kanungo <bikash at umich.edu>
>>>>> wrote:
>>>>>
>>>>>> Hi Patrick,
>>>>>>
>>>>>> Thanks for the clarification. I want it to be an efficient operation.
>>>>>> The idea is to convert a generalized eigenvalue problem (H*x = lamda*M*x)
>>>>>> to a standard one (M^{-1}*H*x = lambda*x). The M^{-1} is of type MATNEST
>>>>>> whereas H is MPIAIJ. In my problem M^{-1} is computed once whereas H
>>>>>> changes every iteration. So performing low-level matrix multiplication or
>>>>>> creating H as MATNEST and assembling it from its sub-matrices at every
>>>>>> iteration sounds inefficient.
>>>>>>
>>>>>
>>>>> You can solve these types of things with SLEPc without explicit MatMat.
>>>>>
>>>>>    Matt
>>>>>
>>>>>
>>>>>> Regards,
>>>>>> Bikash
>>>>>>
>>>>>> On Mon, May 11, 2015 at 9:21 AM, Patrick Sanan <
>>>>>> patrick.sanan at gmail.com> wrote:
>>>>>>
>>>>>>> MatMatMult with MATNEST does not seem to be supported, based only on
>>>>>>> the fact that there are no functions of the form MatMatMult_*_MatNest
>>>>>>> defined with the MATNEST implementation :
>>>>>>> http://www.mcs.anl.gov/petsc/petsc-current/src/mat/impls/nest/matnest.c.html
>>>>>>>
>>>>>>> Does this operation need to be efficient? (That is, are you forming
>>>>>>> this matrix for debugging or experimental purposes, or with the intention
>>>>>>> of using it within an efficient, scalable piece of code?). If not, it
>>>>>>> should be possible to use lower-level matrix operations as defined by the
>>>>>>> API to extract the appropriately-sized submatrices from A and (assuming
>>>>>>> that MatMatMult is defined between MATMPIAIJ and the submatrices of your
>>>>>>> Matnest), perform the matrix multiplications and additions "by hand" .
>>>>>>>
>>>>>>> On Mon, May 11, 2015 at 2:44 PM, Bikash Kanungo <bikash at umich.edu>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> I have two matrices: A of type MPIAIJ and B of type MATNEST. Is
>>>>>>>> there any way to perform A*B after B has been assembled from its
>>>>>>>> sub-matrices?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Bikash
>>>>>>>>
>>>>>>>> --
>>>>>>>> Bikash S. Kanungo
>>>>>>>> PhD Student
>>>>>>>> Computational Materials Physics Group
>>>>>>>> Mechanical Engineering
>>>>>>>> University of Michigan
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Bikash S. Kanungo
>>>>>> PhD Student
>>>>>> Computational Materials Physics Group
>>>>>> Mechanical Engineering
>>>>>> University of Michigan
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> 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
>>>>>
>>>>
>>>
>
>
> --
> Bikash S. Kanungo
> PhD Student
> Computational Materials Physics Group
> Mechanical Engineering
> University of Michigan
>
>
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