Thanks, Jose. I was able to get this to work with the largest eigenvalues option.
Thanks! -Manav > On Sep 7, 2018, at 2:04 AM, Jose E. Roman <[email protected]> wrote: > > The matrices are not in a format that I can easily load. Anyway, I see that > you are using "smallest magnitude", which will give bad convergence in most > cases. It is specially not recommended in generalized eigenproblems: instead > of smallest eigenvalues of (M0,M1) you should compute largest eigenvalues of > (M1,M0), and evaluate the reciprocals of the eigenvalues. Alternatively, use > shift-and-invert to compute eigenvalues of (M0,M1) closest to the target > sigma=0. If M0 is singular, then use a small nonzero value for sigma. > > Jose > > >> El 7 sept 2018, a las 8:44, Manav Bhatia <[email protected]> escribió: >> >> Hi, >> >> I am attempting to compute the eigenvalues of the generalized nonhermitian >> eigenproblem >> >> M1 x = lambda M0 x >> >> M1 and M0 are in the attached text files. The solver is unable to converge >> with 300 iterations. I have played around with the max it (as high as 1500) >> and it still is unable to converge. >> >> What would be the best way to work around this issue? >> >> I would appreciate your help. >> >> Thanks, >> Manav >> >> <M0.txt> >> <M1.txt> >> >> >> EPS Object: 1 MPI processes >> type: krylovschur >> 50% of basis vectors kept after restart >> using the locking variant >> problem type: generalized non-symmetric eigenvalue problem >> selected portion of the spectrum: smallest eigenvalues in magnitude >> number of eigenvalues (nev): 10 >> number of column vectors (ncv): 50 >> maximum dimension of projected problem (mpd): 50 >> maximum number of iterations: 300 >> tolerance: 1e-08 >> convergence test: relative to the eigenvalue >> BV Object: 1 MPI processes >> type: svec >> 51 columns of global length 178 >> vector orthogonalization method: classical Gram-Schmidt >> orthogonalization refinement: if needed (eta: 0.7071) >> block orthogonalization method: GS >> doing matmult as a single matrix-matrix product >> DS Object: 1 MPI processes >> type: nhep >> ST Object: 1 MPI processes >> type: shift >> shift: 0. >> number of matrices: 2 >> all matrices have different nonzero pattern >> KSP Object: (st_) 1 MPI processes >> type: preonly >> maximum iterations=10000, initial guess is zero >> tolerances: relative=1e-08, absolute=1e-50, divergence=10000. >> left preconditioning >> using NONE norm type for convergence test >> PC Object: (st_) 1 MPI processes >> type: lu >> out-of-place factorization >> tolerance for zero pivot 2.22045e-14 >> matrix ordering: nd >> factor fill ratio given 0., needed 0. >> Factored matrix follows: >> Mat Object: 1 MPI processes >> type: seqdense >> rows=178, cols=178 >> package used to perform factorization: petsc >> total: nonzeros=31684, allocated nonzeros=31684 >> total number of mallocs used during MatSetValues calls =0 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqdense >> rows=178, cols=178 >> total: nonzeros=31684, allocated nonzeros=31684 >> total number of mallocs used during MatSetValues calls =0 >> Linear eigensolve did not converge due to DIVERGED_ITS; iterations 300 >
