I've implemented my application using MatGetSubMatrix and the solvers appear to be converging correctly now, just slowly. I assume that this is due to the clustering of eigenvalues inherent to the problem that I'm using, however. I think that this should be enough to get me on track to solving problems with it.

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Thanks, Chris On 10/14/16 01:43, Christopher Pierce wrote: > 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 <cmpie...@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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