1. I was able to use MatIsHermitian function successfully since my matrix type is seqaij. Just hoped that it can return MatNorm (H -H^+) with this function. But it wasn't hard for me to implement that with the function listed in the previous email. 2. I have a related question. In user manual https://slepc.upv.es/documentation/slepc.pdf 2.1, it says that Ax=\lambda B x problem is usually reformulated to B^-1 Ax =\lambda x. If A matrix is Hermitian, B is diagonal but Bii and Bjj can be different.
a) Will solving "Ax=\lambda B x" directly with slepc guarantees users receiving orthogonal eigenvectors? Namely, does xi^T*B*xj=delta_ij hold true? b) if we reformulate, (B^-1A) x = \lambda x will yield (B^-1 A) to be non-hermitian and therefore doesn't give orthogonal eigenvectors ( pointed out by Jose). Xi^T*xj != delta_ij. What about xi^T*B*xj=delta_ij, will this be guaranteed(since this is the same problem as "a" )? Currently, my test is that xi^T*B*xj=delta_ij is no longer true. To help with visibility: [cid:[email protected]] Although a and b are the same problem formulated differently, but the orthogonality isn't ensured in the case b while in case a is ensured(?) . If so, does it mean that we should be encouraged to use case a ? Best, Kuang From: Zhang, Hong <[email protected]> Sent: Thursday, December 2, 2021 2:18 PM To: Wang, Kuang-chung <[email protected]>; Jose E. Roman <[email protected]> Cc: [email protected]; Obradovic, Borna <[email protected]>; Cea, Stephen M <[email protected]> Subject: Re: [petsc-users] Orthogonality of eigenvectors in SLEPC Kuang, PETSc supports MatIsHermitian() for SeqAIJ, IS and SeqSBAIJ matrix types. What is your matrix type? We should be able to add this support to other mat types. Hong ________________________________ From: petsc-users <[email protected]<mailto:[email protected]>> on behalf of Wang, Kuang-chung <[email protected]<mailto:[email protected]>> Sent: Thursday, December 2, 2021 2:06 PM To: Jose E. Roman <[email protected]<mailto:[email protected]>> Cc: [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>>; Obradovic, Borna <[email protected]<mailto:[email protected]>>; Cea, Stephen M <[email protected]<mailto:[email protected]>> Subject: Re: [petsc-users] Orthogonality of eigenvectors in SLEPC Thanks Jose for your prompt reply. I did find my matrix highly non-hermitian. By forcing the solver to be hermtian, the orthogonality was restored. But I do need to root cause why my matrix is non-hermitian in the first place. Along the way, I highly recommend MatIsHermitian() function or combining functions like MatHermitianTranspose () MatAXPY MatNorm to determine the hermiticity to safeguard our program. Best, Kuang -----Original Message----- From: Jose E. Roman <[email protected]<mailto:[email protected]>> Sent: Wednesday, November 24, 2021 6:20 AM To: Wang, Kuang-chung <[email protected]<mailto:[email protected]>> Cc: [email protected]<mailto:[email protected]>; Obradovic, Borna <[email protected]<mailto:[email protected]>>; Cea, Stephen M <[email protected]<mailto:[email protected]>> Subject: Re: [petsc-users] Orthogonality of eigenvectors in SLEPC In Hermitian eigenproblems orthogonality of eigenvectors is guaranteed/enforced. But you are solving the problem as non-Hermitian. If your matrix is Hermitian, make sure you solve it as a HEP, and make sure that your matrix is numerically Hermitian. If your matrix is non-Hermitian, then you cannot expect the eigenvectors to be orthogonal. What you can do in this case is get an orthogonal basis of the computed eigenspace, see https://slepc.upv.es/documentation/current/docs/manualpages/EPS/EPSGetInvariantSubspace.html By the way, version 3.7 is more than 5 years old, it is better if you can upgrade to a more recent version. Jose > El 24 nov 2021, a las 7:15, Wang, Kuang-chung > <[email protected]<mailto:[email protected]>> escribió: > > 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.ht > ml > > 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: > * slepc/3.7.4 > * 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
