Name
Abstract | ||
|---|---|---|
We consider hybrid deterministic-stochastic iterative algorithms for the solution of large, sparse linear systems. Starting from a convergent splitting of the coefficient matrix, we analyze various types of Monte Carlo acceleration schemes applied to the original preconditioned Richardson (stationary) iteration. These methods are expected to have considerable potential for resiliency to faults when implemented on massively parallel machines. We establish sufficient conditions for the convergence of the hybrid schemes, and we investigate different types of preconditioners including sparse approximate inverses. Numerical experiments on linear systems arising from the discretization of partial differential equations are presented. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1002/nla.2088 | NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS |
Keywords | Field | DocType |
iterative methods,Monte Carlo methods,preconditioning,resilience,Richardson iteration,sparse approximate inverses,sparse linear systems | Monte Carlo method,Mathematical optimization,Coefficient matrix,Modified Richardson iteration,Preconditioner,Iterative method,Sparse approximation,Relaxation (iterative method),Mathematics,Matrix-free methods | Journal |
Volume | Issue | ISSN |
24.0 | 3.0 | 1070-5325 |
Citations | PageRank | References |
4 | 0.52 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Benzi | 1 | 1336 | 94.97 |
| Thomas M. Evans | 2 | 62 | 13.58 |
| Steven P. Hamilton | 3 | 19 | 4.22 |
| Massimiliano Lupo Pasini | 4 | 4 | 1.54 |
| Stuart R. Slattery | 5 | 4 | 0.52 |