(1) Parallel Computing Group, Physics and Mathematics Department, Aerospatiale, 12 Rue Pasteur, BP 76, F-92150 Suresnes Cedex, France;(2) Parallel Algorithms Project, CERFACS, 42 Ave. G. Coriolis, F-31057 Toulouse Cedex, France
Abstract:
We investigate the use of sparse approximate inverse preconditioners for the iterative solution of linear systems with dense
complex coefficient matrices arising in industrial electromagnetic problems. An approximate inverse is computed via a Frobenius
norm approach with a prescribed nonzero pattern. Some strategies for determining the nonzero pattern of an approximate inverse
are described. The results of numerical experiments suggest that sparse approximate inverse preconditioning is a viable approach
for the solution of large-scale dense linear systems on parallel computers.
This revised version was published online in August 2006 with corrections to the Cover Date.