[petsc-users] SLEPc: Convergence Problems

Christopher Pierce cmpierce at WPI.EDU
Fri Oct 14 00:43:35 CDT 2016


Thank You,

That looks like what I need to do if the highly degenerate eigenpairs
are my problem.  I'll try that out this week and see if that helps.

Chris




On 10/13/16 20:01, Barry Smith wrote:
>   I would use MatGetSubMatrix() to pull out the part of the matrix you care about and hand that matrix off to SLEPc.
>
>   Others prefer to remove the Dirichlet boundary value locations while doing the finite element assembly, this way those locations never appear in the matrix. 
>
>    The end result is the same, you have the slightly smaller matrix of interest to compute the eigenvalues from.
>
>
> Barry
>
>> On Oct 13, 2016, at 5:48 PM, Christopher Pierce <cmpierce at WPI.EDU> wrote:
>>
>> Hello All,
>>
>> As there isn't a SLEPc specific list, it was recommended that I bring my
>> question here.  I am using SLEPc to solve a generalized eigenvalue
>> problem generated as part of the Finite Element Method, but am having
>> difficulty getting the diagonalizer to converge.  I am worried that the
>> method used to set boundary conditions in the matrix is creating the
>> problem and am looking for other people's input.
>>
>> In order to set the boundary conditions, I find the list of IDs that
>> should be zero in the resulting eigenvectors and then use
>> MatZeroRowsColumns to zero the rows and columns and in the matrix A
>> insert a large value such as 1E10 on each diagonal element that was
>> zeroed and likewise for the B matrix except with the value 1.0.  That
>> way the eigenvalues resulting from those solutions are on the order of
>> 1E10 and are outside of the region of interest for my problem.
>>
>> When I tried to diagonal the matrices I could only get converged
>> solutions from the rqcg method which I have found to not scale well with
>> my problem.  When using any other method, the approximate error of the
>> eigenpairs hovers around 1E00 and 1E01 until it reaches the max number
>> of iterations.  Could having so many identical eigenvalues (~1,000) in
>> the spectrum be causing this to happen even if they are far outside of
>> the range of interest?
>>
>> Thank,
>>
>> Chris Pierce
>> WPI Center for Computation Nano-Science
>>
>>


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