SPMR: A Family of Saddle-Point Minimum Residual Solvers
| Autor: | Ron Estrin, Chen Greif |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Applied Mathematics
Monotonic function 010103 numerical & computational mathematics Residual 01 natural sciences Symmetry (physics) 010101 applied mathematics Computational Mathematics Matrix (mathematics) Bidiagonalization Saddle point Applied mathematics 0101 mathematics Saddle Subspace topology Mathematics |
| Zdroj: | SIAM Journal on Scientific Computing. 40:A1884-A1914 |
| ISSN: | 1095-7197 1064-8275 |
| DOI: | 10.1137/16m1102410 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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