Dear all, I am using PETSC and SLEPC to simulate a problem in MHD, described in http://scitation.aip.org/content/aip/journal/pop/21/4/10.1063/1.4871859.
I have a bit of experience with MPI but not too much with PETSC and SLEPC. So after reading both user manuals and also the relevant chapters of the PETSC developers manual, I still can't get it to work. The problem that I have to solve is a large generalized eigenvalue system where the matrices are both Hermitian* by blocks* and *tridiagonal*, e.g.: ( A11 A12 0 0 0 ) ( B11 B12 0 0 0 ) ( A12* A22 A23 0 0 ) ( B12* B22 B23 0 0 ) ( 0 A23* A33 A34 0 ) = lambda ( 0 B23* B33 B34 0 ) ( 0 0 A34* A44 A45) ( 0 0 B34* B44 B45) ( 0 0 A45* A55 0 ) ( 0 0 B45* B55 0 ) where Aii = Aii*, with * the Hermitian conjugate. I apologize for the ugly representation. The dimensions of both A and B are around 50 to 100 blocks (as there is a block per discretized point) and the blocks themselves can vary from 1 to more than 100x100 as well (as they correspond to a spectral decomposition). Now, my question is: how to solve this economically? What I have been trying to do is to make use of the fact that the matrices are Hermitian and by using *matcreatesbaij* and through the recommended *matcreate*, *matsettype(matsbaij)*, etc. Could someone help me out? All help would be greatly appreciated! Thank you in advance, Toon UC3M
