[petsc-users] Orthogonality of eigenvectors in SLEPC
Lucas Banting
bantingl at myumanitoba.ca
Wed Nov 24 09:50:15 CST 2021
Hi Kuang-Chung Wang ,
It says your eigenvalue problem type is:
problem type: non-hermitian eigenvalue problem
While the email chain you refer to is for a hermitian eigenvalue problem.
Try changing your eigenvalue problem type to a hermitian or generalized hermitian problem and it should produce orthogonal eigenvectors.
Lucas
________________________________
From: petsc-users <petsc-users-bounces at mcs.anl.gov> on behalf of Wang, Kuang-chung <kuang-chung.wang at intel.com>
Sent: Wednesday, November 24, 2021 12:15 AM
To: petsc-users at mcs.anl.gov <petsc-users at mcs.anl.gov>
Cc: Obradovic, Borna <borna.obradovic at intel.com>; Cea, Stephen M <stephen.m.cea at intel.com>
Subject: Re: [petsc-users] Orthogonality of eigenvectors in SLEPC
Dear Jose :
I came across this thread describing issue using krylovschur and finding eigenvectors non-orthogonal.
https://lists.mcs.anl.gov/pipermail/petsc-users/2014-October/023360.html
I furthermore have tested by reducing the tolerance as highlighted below from 1e-12 to 1e-16 with no luck.
Could you please suggest options/sources to try out ?
Thanks a lot for sharing your knowledge!
Sincere,
Kuang-Chung Wang
=======================================================
Kuang-Chung Wang
Computational and Modeling Technology
Intel Corporation
Hillsboro OR 97124
=======================================================
Here are more info:
1. slepc/3.7.4
2. output message from by doing EPSView(eps,PETSC_NULL):
EPS Object: 1 MPI processes
type: krylovschur
Krylov-Schur: 50% of basis vectors kept after restart
Krylov-Schur: using the locking variant
problem type: non-hermitian eigenvalue problem
selected portion of the spectrum: closest to target: 20.1161 (in magnitude)
number of eigenvalues (nev): 40
number of column vectors (ncv): 81
maximum dimension of projected problem (mpd): 81
maximum number of iterations: 1000
tolerance: 1e-12
convergence test: relative to the eigenvalue
BV Object: 1 MPI processes
type: svec
82 columns of global length 2988
vector orthogonalization method: classical Gram-Schmidt
orthogonalization refinement: always
block orthogonalization method: Gram-Schmidt
doing matmult as a single matrix-matrix product
DS Object: 1 MPI processes
type: nhep
ST Object: 1 MPI processes
type: sinvert
shift: 20.1161
number of matrices: 1
KSP Object: (st_) 1 MPI processes
type: preonly
maximum iterations=1000, initial guess is zero
tolerances: relative=1.12005e-09, absolute=1e-50, divergence=10000.
left preconditioning
using NONE norm type for convergence test
PC Object: (st_) 1 MPI processes
type: lu
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: seqaij
rows=2988, cols=2988
package used to perform factorization: mumps
total: nonzeros=614160, allocated nonzeros=614160
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) (sequential matrix ordering):7
ICNTL(8) (scaling 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): 0
ICNTL(19) (Schur complement info): 0
ICNTL(20) (rhs sparse pattern): 0
ICNTL(21) (solution struct): 0
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] 8.15668e+07
RINFO(2) (local estimated flops for the assembly after factorization):
[0] 892584.
RINFO(3) (local estimated flops for the elimination after factorization):
[0] 8.15668e+07
INFO(15) (estimated size of (in MB) MUMPS internal data for running numerical factorization):
[0] 16
INFO(16) (size of (in MB) MUMPS internal data used during numerical factorization):
[0] 16
INFO(23) (num of pivots eliminated on this processor after factorization):
[0] 2988
RINFOG(1) (global estimated flops for the elimination after analysis): 8.15668e+07
RINFOG(2) (global estimated flops for the assembly after factorization): 892584.
RINFOG(3) (global estimated flops for the elimination after factorization): 8.15668e+07
(RINFOG(12) RINFOG(13))*2^INFOG(34) (determinant): (0.,0.)*(2^0)
INFOG(3) (estimated real workspace for factors on all processors after analysis): 614160
INFOG(4) (estimated integer workspace for factors on all processors after analysis): 31971
INFOG(5) (estimated maximum front size in the complete tree): 246
INFOG(6) (number of nodes in the complete tree): 197
INFOG(7) (ordering option effectively use after analysis): 2
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): 614160
INFOG(10) (total integer space store the matrix factors after factorization): 31971
INFOG(11) (order of largest frontal matrix after factorization): 246
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): 16
INFOG(17) (estimated size of all MUMPS internal data for factorization after analysis: sum over all processors): 16
INFOG(18) (size of all MUMPS internal data allocated during factorization: value on the most memory consuming processor): 16
INFOG(19) (size of all MUMPS internal data allocated during factorization: sum over all processors): 16
INFOG(20) (estimated number of entries in the factors): 614160
INFOG(21) (size in MB of memory effectively used during factorization - value on the most memory consuming processor): 14
INFOG(22) (size in MB of memory effectively used during factorization - sum over all processors): 14
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)): 614160
INFOG(30, 31) (after solution: size in Mbytes of memory used during solution phase): 13, 13
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: 1 MPI processes
type: seqaij
rows=2988, cols=2988
total: nonzeros=151488, allocated nonzeros=151488
total number of mallocs used during MatSetValues calls =0
using I-node routines: found 996 nodes, limit used is 5
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