Here is a stack I end up with when trying to solve an eigen problem (real, sym, generalized) with SLEPc. My understanding is that, during the Gram Schmidt orthogonalisation, the projection of one basis vector turns out to be null. First, is this correct ? Second, in such cases, are there some recommended "recipe" to test/try (options) to get a clue on the problem ? (I would unfortunately perfectly understand the answer could be no !... As this totally depends on A/B).
With arpack, the eigen problem is solved (so the matrix A and B I use seems to be relevant). But, when I change from arpack to krylovschur/ciss/arnoldi, I get the stack below. Franck [0]PETSC ERROR: #1 BV_SafeSqrt() [0]PETSC ERROR: #2 BVNorm _Private() [0]PETSC ERROR: #3 BVNormColumn() [0]PETSC ERROR: #4 BV_NormVecOrColumn() [0]PETSC ERROR: #5 BVOrthogonalizeCGS1() [0]PETSC ERROR: #6 BVOrthogonalizeGS () [0]PETSC ERROR: #7 BVOrthonormalizeColumn() [0]PETSC ERROR: #8 EPSFullLanczos() [0]PETSC ERROR: #9 EPSSolve_KrylovSchur_Symm() [0]PETSC ERROR: #10 EPSSolve()
