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Preconditioners for rank deficient least squares problems

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Preconditioners for rank deficient least squares problems

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dc.contributor.author Cerdán Soriano, Juana Mercedes es_ES
dc.contributor.author Guerrero, D. es_ES
dc.contributor.author Marín Mateos-Aparicio, José es_ES
dc.contributor.author Mas Marí, José es_ES
dc.date.accessioned 2021-02-19T04:33:51Z
dc.date.available 2021-02-19T04:33:51Z
dc.date.issued 2020-07 es_ES
dc.identifier.issn 0377-0427 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161853
dc.description.abstract [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. es_ES
dc.description.sponsorship This work was supported by the Spanish Ministerio de Economia, Industria y Competitividad, Spain under grants MTM2017-85669-P and MTM2017-90682-REDT. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Computational and Applied Mathematics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Iterative methods es_ES
dc.subject Rank deficient es_ES
dc.subject Sparse linear systems es_ES
dc.subject Preconditioning es_ES
dc.subject Linear least squares problems es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Preconditioners for rank deficient least squares problems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cam.2019.112621 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-85669-P/ES/PROBLEMAS MATRICIALES: COMPUTACION, TEORIA Y APLICACIONES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//MTM2017-90682-REDT/ES/RED TEMATICA DE ALGEBRA LINEAL, ANALISIS MATRICIAL Y APLICACIONES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Cerdán Soriano, JM.; Guerrero, D.; Marín Mateos-Aparicio, J.; Mas Marí, J. (2020). Preconditioners for rank deficient least squares problems. Journal of Computational and Applied Mathematics. 372:1-11. https://doi.org/10.1016/j.cam.2019.112621 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cam.2019.112621 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 11 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 372 es_ES
dc.relation.pasarela S\413884 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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