Thanks, Those changes did improve the tolerances of the solutions. However, I still have the same problem. For certain matrices the error is up to 10^4 times as large as the requested tolerances and when using true residual the solver gets stuck on a certain residual norm the solutions and does not converge. I dumped the settings that I used which I'm attaching here.
Chris On 02/17/17 04:42, Jose E. Roman wrote: > For computing eigenvalues with smallest real part of generalized problems > Ax=lambda Bx, it may be better to use a target value (instead of > -eps_smallest_real). For instance, if you know that all eigenvalues are > positive, use -eps_target 0 -eps_target_magnitude > > What linear solvers are you using? In the default setting, the coefficient > matrix for linear solves will be B, but with target=sigma the coefficient > matrix will be A-sigma*B; this may make a difference. Also, in any case, if > experiencing convergence problems I would suggest using MUMPS (see section > 3.4.1 of SLEPc's users manual). > > Jose > > > >> El 17 feb 2017, a las 10:25, Christopher Pierce <cmpie...@wpi.edu> escribió: >> >> Hello All, >> >> I'm trying to use the SLEPc Krylov-Schur implementation to solve a >> general eigenvalue problem. I have a monitor on my solver and the >> solutions appear to converge correctly when using the approximation for >> the residual norm in the algorithm. However, when the solutions are >> displayed and I retrieve the actual residual norm it is very large and >> increases with the size of the matrices I am working with. In some >> cases it may be 10^17 times as large as the approximate norm. I also >> don't get the eigenvalues I would expect for the system I am studying. >> >> When I turn on the option "true residual" the solver fails to converge. >> The residual norm shrinks to some limit (~10^-3) and then sits there for >> the remaining iterations. As a note, I am solving for the eigenvalues >> with the smallest real part. I have also tried the RQCG solver on the >> same problems and appear to get the correct results using it, but I'm >> looking to use the better scaling of the Krylov-Schur solver. >> >> Does anyone know what could be causing this behavior? >> >> Thanks, >> >> Chris Pierce >> WPI Center for Computational Nanoscience >> >>
EPS Object: 4 MPI processes type: krylovschur Krylov-Schur: 50% of basis vectors kept after restart Krylov-Schur: using the locking variant problem type: generalized symmetric eigenvalue problem selected portion of the spectrum: closest to target: 0. (in magnitude) postprocessing eigenvectors with purification number of eigenvalues (nev): 10 number of column vectors (ncv): 25 maximum dimension of projected problem (mpd): 25 maximum number of iterations: 1000 tolerance: 1e-10 convergence test: relative to the eigenvalue BV Object: 4 MPI processes type: svec 26 columns of global length 12513 vector orthogonalization method: classical Gram-Schmidt orthogonalization refinement: if needed (eta: 0.7071) block orthogonalization method: Gram-Schmidt non-standard inner product Mat Object: 4 MPI processes type: mpiaij rows=12513, cols=12513 total: nonzeros=177931, allocated nonzeros=177931 total number of mallocs used during MatSetValues calls =0 not using I-node (on process 0) routines doing matmult as a single matrix-matrix product DS Object: 4 MPI processes type: hep solving the problem with: Implicit QR method (_steqr) ST Object: 4 MPI processes type: sinvert shift: 0. number of matrices: 2 all matrices have different nonzero pattern KSP Object: (st_) 4 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_) 4 MPI processes type: lu LU: out-of-place factorization tolerance for zero pivot 2.22045e-14 matrix ordering: natural factor fill ratio given 0., needed 0. Factored matrix follows: Mat Object: 4 MPI processes type: mpiaij rows=12513, cols=12513 package used to perform factorization: mumps total: nonzeros=4234311, allocated nonzeros=4234311 total number of mallocs used during MatSetValues calls =0 MUMPS run parameters: SYM (matrix type): 0 PAR (host participation): 1 ICNTL(1) (output for error): 6 ICNTL(2) (output of diagnostic msg): 0 ICNTL(3) (output for global info): 0 ICNTL(4) (level of printing): 0 ICNTL(5) (input mat struct): 0 ICNTL(6) (matrix prescaling): 7 ICNTL(7) (sequentia matrix ordering):7 ICNTL(8) (scalling strategy): 77 ICNTL(10) (max num of refinements): 0 ICNTL(11) (error analysis): 0 ICNTL(12) (efficiency control): 1 ICNTL(13) (efficiency control): 0 ICNTL(14) (percentage of estimated workspace increase): 20 ICNTL(18) (input mat struct): 3 ICNTL(19) (Shur complement info): 0 ICNTL(20) (rhs sparse pattern): 0 ICNTL(21) (solution struct): 1 ICNTL(22) (in-core/out-of-core facility): 0 ICNTL(23) (max size of memory can be allocated locally):0 ICNTL(24) (detection of null pivot rows): 0 ICNTL(25) (computation of a null space basis): 0 ICNTL(26) (Schur options for rhs or solution): 0 ICNTL(27) (experimental parameter): -24 ICNTL(28) (use parallel or sequential ordering): 1 ICNTL(29) (parallel ordering): 0 ICNTL(30) (user-specified set of entries in inv(A)): 0 ICNTL(31) (factors is discarded in the solve phase): 0 ICNTL(33) (compute determinant): 0 CNTL(1) (relative pivoting threshold): 0.01 CNTL(2) (stopping criterion of refinement): 1.49012e-08 CNTL(3) (absolute pivoting threshold): 0. CNTL(4) (value of static pivoting): -1. CNTL(5) (fixation for null pivots): 0. RINFO(1) (local estimated flops for the elimination after analysis): [0] 3.42689e+08 [1] 5.94214e+08 [2] 3.8211e+08 [3] 3.4841e+08 RINFO(2) (local estimated flops for the assembly after factorization): [0] 1.5205e+06 [1] 1.4933e+06 [2] 1.4988e+06 [3] 1.56079e+06 RINFO(3) (local estimated flops for the elimination after factorization): [0] 3.42689e+08 [1] 5.94214e+08 [2] 3.8211e+08 [3] 3.4841e+08 INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization): [0] 37 [1] 44 [2] 40 [3] 38 INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization): [0] 37 [1] 44 [2] 40 [3] 38 INFO(23) (num of pivots eliminated on this processor after factorization): [0] 3977 [1] 2646 [2] 2409 [3] 3481 RINFOG(1) (global estimated flops for the elimination after analysis): 1.66742e+09 RINFOG(2) (global estimated flops for the assembly after factorization): 6.0734e+06 RINFOG(3) (global estimated flops for the elimination after factorization): 1.66742e+09 (RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (0.,0.)*(2^0) INFOG(3) (estimated real workspace for factors on all processors after analysis): 4234311 INFOG(4) (estimated integer workspace for factors on all processors after analysis): 169823 INFOG(5) (estimated maximum front size in the complete tree): 925 INFOG(6) (number of nodes in the complete tree): 2357 INFOG(7) (ordering option effectively use after analysis): 4 INFOG(8) (structural symmetry in percent of the permuted matrix after analysis): 100 INFOG(9) (total real/complex workspace to store the matrix factors after factorization): 4234311 INFOG(10) (total integer space store the matrix factors after factorization): 169823 INFOG(11) (order of largest frontal matrix after factorization): 925 INFOG(12) (number of off-diagonal pivots): 0 INFOG(13) (number of delayed pivots after factorization): 0 INFOG(14) (number of memory compress after factorization): 0 INFOG(15) (number of steps of iterative refinement after solution): 0 INFOG(16) (estimated size (in MB) of all MUMPS internal data for factorization after analysis: value on the most memory consuming processor): 44 INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): 159 INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): 44 INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): 159 INFOG(20) (estimated number of entries in the factors): 4234311 INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): 38 INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): 142 INFOG(23) (after analysis: value of ICNTL(6) effectively used): 0 INFOG(24) (after analysis: value of ICNTL(12) effectively used): 1 INFOG(25) (after factorization: number of pivots modified by static pivoting): 0 INFOG(28) (after factorization: number of null pivots encountered): 0 INFOG(29) (after factorization: effective number of entries in the factors (sum over all processors)): 4234311 INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): 22, 68 INFOG(32) (after analysis: type of analysis done): 1 INFOG(33) (value used for ICNTL(8)): 7 INFOG(34) (exponent of the determinant if determinant is requested): 0 linear system matrix = precond matrix: Mat Object: 4 MPI processes type: mpiaij rows=12513, cols=12513 total: nonzeros=177931, allocated nonzeros=177931 total number of mallocs used during MatSetValues calls =0 not using I-node (on process 0) routines