As far as I know, the solveForward/Backward operations are not implemented for external packages.
What are you trying to do? One may be tempted to use the Cholesky decomposition to transform a symmetric-definite generalized eigenvalue problem into a standard eigenvalue problem, as is done e.g. in LAPACK https://netlib.org/lapack/lug/node54.html - But note that in SLEPc you do not need to do this, as EPSSolve() will take care of symmetry using Cholesky without having to apply solveForward/Backward separately. Jose > El 2 dic 2022, a las 13:32, Johannes Haubner <[email protected]> escribió: > > Given a s.p.d. matrix A and a right-hand-side b, we want to compute > (L^T)^{-1}b and L^{-1}b, where A= L L^T is the Cholesky decomposition of A. > In serial, we are able to do this using pc_factor_mat_solver_type petsc and > using "solveForward" and "solveBackward" (see > https://github.com/JohannesHaubner/petsc-cholesky/blob/main/main.py). In > parallel, we are not able to do this yet. According to p. 52 > https://slepc.upv.es/documentation/slepc.pdf , PETSc's Cholesky factorization > is not parallel. However, using "mumps" or "superlu_dist" prevents us from > using "solveForward" or "solveBackward". Is there a way of using > solveBackward in parallel with a distributed matrix (MPI)?
