Sparse approximate inverse preconditioning for dense linear systems arising in computational electromagnetics

被引：89
|
作者：
Alleon, G
Benzi, M
Giraud, L
机构：
[1] Aerospatiale, Dept Math & Phys, Parallel Comp Grp, F-92150 Suresnes, France
[2] CERFACS, Parallel Algorithms Project, F-31057 Toulouse, France
来源：
NUMERICAL ALGORITHMS | 1997年 / 16卷 / 01期
关键词：
dense linear systems; preconditioning; sparse approximate inverses; complex symmetric matrices; scattering calculations; Krylov subspace methods; parallel computing;
D O I：
10.1023/A:1019170609950
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
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页码：1 / 15
页数：15
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