The State-of-the-Art of Preconditioners for Sparse Linear Least-Squares Problems
| Autor: | Nicholas I. M. Gould, Jennifer A. Scott |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Applied Mathematics Diagonal Relaxation (iterative method) 010103 numerical & computational mathematics Sparse approximation Krylov subspace Computer Science::Numerical Analysis 01 natural sciences Mathematics::Numerical Analysis 010101 applied mathematics Simple (abstract algebra) Direct methods Applied mathematics 0101 mathematics Software Linear least squares Sparse matrix Mathematics |
| Zdroj: | ACM Transactions on Mathematical Software. 43:1-35 |
| ISSN: | 1557-7295 0098-3500 |
| Popis: | In recent years, a variety of preconditioners have been proposed for use in solving large sparse linear least-squares problems. These include simple diagonal preconditioning, preconditioners based on incomplete factorizations, and stationary inner iterations used with Krylov subspace methods. In this study, we briefly review preconditioners for which software has been made available, then present a numerical evaluation of them using performance profiles and a large set of problems arising from practical applications. Comparisons are made with state-of-the-art sparse direct methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|