Name
Abstract | ||
|---|---|---|
We introduce a new family of saddle-point minimum residual methods for iteratively solving saddle-point systems using a minimum or quasi-minimum residual approach. No symmetry assumptions are made. The basic mechanism underlying the method is a novel simultaneous bidiagonalization procedure that yields a simplified saddle-point matrix on a projected Krylov-like subspace and allows for a monotonic short-recurrence iterative scheme. We develop a few variants, demonstrate the advantages of our approach, derive optimality conditions, and discuss connections to existing methods. Numerical experiments illustrate the merits of this new family of methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1137/16M1102410 | SIAM JOURNAL ON SCIENTIFIC COMPUTING |
Keywords | Field | DocType |
saddle-point systems,iterative solvers,Krylov subspaces,bidiagonalization,minimum residual,preconditioning | Saddle,Monotonic function,Residual,Mathematical optimization,Saddle point,Subspace topology,Matrix (mathematics),Bidiagonalization,Mathematics | Journal |
Volume | Issue | ISSN |
40 | 3 | 1064-8275 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ron Estrin | 1 | 0 | 1.35 |
| CHEN GREIF | 2 | 321 | 43.63 |