Name
Abstract | ||
|---|---|---|
We consider different preconditioning techniques of both implicit and explicit form in connection with Krylov methods for the solution of large dense complex symmetric non-Hermitian systems of equations arising in computational electromagnetics. We emphasize in particular sparse approximate inverse techniques that use a static nonzero pattern selection. By exploiting geometric information from the underlying meshes, a very sparse but effective preconditioner can be computed. In particular our strategies are applicable when fast multipole methods are used for the matrix-vector products on parallel distributed memory computers. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45262-1_21 | NAA |
Keywords | Field | DocType |
krylov method,robust preconditioning,effective preconditioner,matrix-vector product,sparse approximate inverse technique,large dense complex symmetric,memory computer,dense problems,geometric information,different preconditioning technique,computational electromagnetics,explicit form | Applied mathematics,Mathematical optimization,Computational electromagnetics,Polygon mesh,Preconditioner,System of linear equations,Shared memory,Computer science,Electromagnetics,Distributed memory,Matrix multiplication,Distributed computing | Conference |
Volume | ISSN | ISBN |
1988 | 0302-9743 | 3-540-41814-8 |
Citations | PageRank | References |
1 | 0.42 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Carpentieri | 1 | 136 | 12.01 |
| Iain S. Duff | 2 | 1107 | 148.90 |
| Luc Giraud | 3 | 393 | 63.00 |