Preconditioners for rank deficient least squares problems
[EN] In this paper we present a method for computing sparse preconditioners for iteratively solving rank deficient least squares problems (LS) by the LSMR method. The main idea of the method proposed is to update an incomplete factorization computed for a regularized problem to recover the solution...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/161853 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/161853 |
| Access Level: | acceso abierto |
| Palabra clave: | Iterative methods Rank deficient Sparse linear systems Preconditioning Linear least squares problems MATEMATICA APLICADA |
| Sumario: | [EN] In this paper we present a method for computing sparse preconditioners for iteratively solving rank deficient least squares problems (LS) by the LSMR method. The main idea of the method proposed is to update an incomplete factorization computed for a regularized problem to recover the solution of the original one. The numerical experiments for a wide set of matrices arising from different science and engineering applications show that the preconditioner proposed, in most cases, can be successfully applied to accelerate the convergence of the iterative Krylov subspace method. |
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