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A preconditioner for a primal-dual Newton conjugate gradients method for compressed sensing problems

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dc.contributor.author Dassios, Ioannis K.
dc.contributor.author Fountoulakis, Kimon
dc.contributor.author Gondzio, Jacek
dc.date.accessioned 2016-06-22T14:44:07Z
dc.date.available 2016-06-22T14:44:07Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/10344/5084
dc.description peer-reviewed en_US
dc.description.abstract In this paper we are concerned with the solution of Compressed Sensing (CS) problems where the signals to be recovered are sparse in coherent and redundant dictionaries. We extend the primal-dual Newton Conjugate Gradients (pdNCG) method for CS problems. We provide an inexpensive and provably effective preconditioning technique for linear systems using pdNCG. Numerical results are presented on CS problems which demonstrate the performance of pdNCG with the proposed preconditioner compared to state-of-the-art existing solvers. en_US
dc.language.iso eng en_US
dc.publisher Society for Industrial and Applied Mathematics en_US
dc.relation.ispartofseries SIAM Journal on Scientific Computing;37 (6), pp. A2783-A2812
dc.relation.uri https://ioannisdassios.wordpress.com/research-visits-talks-2/
dc.rights ©2015 SIAM Published by SIAM
dc.subject compressed sensing en_US
dc.subject total-variation en_US
dc.subject second-order methods en_US
dc.title A preconditioner for a primal-dual Newton conjugate gradients method for compressed sensing problems en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2016-06-22T14:27:58Z
dc.description.version PUBLISHED
dc.contributor.sponsor Engineering and Physical Sciences Research Council
dc.relation.projectid EP/I017127/1
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1629272
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


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