Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik
Doi
Abstract
This paper describes a multilevel preconditioning technique for solving
complex symmetric sparse linear systems. The coefficient matrix is first
decoupled by domain decomposition and then an approximate inverse of the
original matrix is computed level by level. This approximate inverse is based
on low rank approximations of the local Schur complements. For this, a
symmetric singular value decomposition of a complex symmetric matix is used.
The block-diagonal matrices are decomposed by an incomplete LDLT
factorization with the Bunch-Kaufman pivoting method. Using the example of
Maxwells equations the generality of the approach is demonstrated
