Parallel projected aggregation methods for solving large inconsistent systems

Autores
Scolnik, Hugo Daniel
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes and/or halfspaces. In [9, 16, 17, 18] we introduced acceleration schemes for solving linear systems within a PAM like framework. The basic idea was to force the next iterate to belong to the convex region de ned by the new separating/or aggregated hyperplane computed in the previous iteration. In this paper we extend the above mentioned methods to the problem of nding the least squares solution to inconsistent systems. In the new algorithm we used a scheme of incomplete alternate projections for minimizing the proximity function, similar to the one of Csisz ar y Tusn ady described in [4] which uses exact projections. The parallel simultaneous projection ACCIM algorithm in [16] is very eÆcient for ob- taining approximations with suitable properties, and is the basis for calculating the incomplete intermediate projections. We discuss the convergence properties of the new algorithm and also present numerical experiences obtained by applying it to image reconstruction problems using the SNARK93 system [3].
Eje: Teoría (TEOR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Parallel
Projected Aggregation Methods
Incomplete Projections
Inconsistent System
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22894

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network_name_str SEDICI (UNLP)
spelling Parallel projected aggregation methods for solving large inconsistent systemsScolnik, Hugo DanielCiencias InformáticasParallelProjected Aggregation MethodsIncomplete ProjectionsInconsistent SystemThe Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes and/or halfspaces. In [9, 16, 17, 18] we introduced acceleration schemes for solving linear systems within a PAM like framework. The basic idea was to force the next iterate to belong to the convex region de ned by the new separating/or aggregated hyperplane computed in the previous iteration. In this paper we extend the above mentioned methods to the problem of nding the least squares solution to inconsistent systems. In the new algorithm we used a scheme of incomplete alternate projections for minimizing the proximity function, similar to the one of Csisz ar y Tusn ady described in [4] which uses exact projections. The parallel simultaneous projection ACCIM algorithm in [16] is very eÆcient for ob- taining approximations with suitable properties, and is the basis for calculating the incomplete intermediate projections. We discuss the convergence properties of the new algorithm and also present numerical experiences obtained by applying it to image reconstruction problems using the SNARK93 system [3].Eje: Teoría (TEOR)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1603-1615http://sedici.unlp.edu.ar/handle/10915/22894enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:36:46Zoai:sedici.unlp.edu.ar:10915/22894Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:36:46.806SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Parallel projected aggregation methods for solving large inconsistent systems
title Parallel projected aggregation methods for solving large inconsistent systems
spellingShingle Parallel projected aggregation methods for solving large inconsistent systems
Scolnik, Hugo Daniel
Ciencias Informáticas
Parallel
Projected Aggregation Methods
Incomplete Projections
Inconsistent System
title_short Parallel projected aggregation methods for solving large inconsistent systems
title_full Parallel projected aggregation methods for solving large inconsistent systems
title_fullStr Parallel projected aggregation methods for solving large inconsistent systems
title_full_unstemmed Parallel projected aggregation methods for solving large inconsistent systems
title_sort Parallel projected aggregation methods for solving large inconsistent systems
dc.creator.none.fl_str_mv Scolnik, Hugo Daniel
author Scolnik, Hugo Daniel
author_facet Scolnik, Hugo Daniel
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel
Projected Aggregation Methods
Incomplete Projections
Inconsistent System
topic Ciencias Informáticas
Parallel
Projected Aggregation Methods
Incomplete Projections
Inconsistent System
dc.description.none.fl_txt_mv The Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes and/or halfspaces. In [9, 16, 17, 18] we introduced acceleration schemes for solving linear systems within a PAM like framework. The basic idea was to force the next iterate to belong to the convex region de ned by the new separating/or aggregated hyperplane computed in the previous iteration. In this paper we extend the above mentioned methods to the problem of nding the least squares solution to inconsistent systems. In the new algorithm we used a scheme of incomplete alternate projections for minimizing the proximity function, similar to the one of Csisz ar y Tusn ady described in [4] which uses exact projections. The parallel simultaneous projection ACCIM algorithm in [16] is very eÆcient for ob- taining approximations with suitable properties, and is the basis for calculating the incomplete intermediate projections. We discuss the convergence properties of the new algorithm and also present numerical experiences obtained by applying it to image reconstruction problems using the SNARK93 system [3].
Eje: Teoría (TEOR)
Red de Universidades con Carreras en Informática (RedUNCI)
description The Projected Aggregation Methods (PAM) for solving linear systems of equali- ties and/or inequalities, generate a new iterate xk+1 by projecting the current point xk onto a separating hyperplane generated by a given linear combination of the original hyperplanes and/or halfspaces. In [9, 16, 17, 18] we introduced acceleration schemes for solving linear systems within a PAM like framework. The basic idea was to force the next iterate to belong to the convex region de ned by the new separating/or aggregated hyperplane computed in the previous iteration. In this paper we extend the above mentioned methods to the problem of nding the least squares solution to inconsistent systems. In the new algorithm we used a scheme of incomplete alternate projections for minimizing the proximity function, similar to the one of Csisz ar y Tusn ady described in [4] which uses exact projections. The parallel simultaneous projection ACCIM algorithm in [16] is very eÆcient for ob- taining approximations with suitable properties, and is the basis for calculating the incomplete intermediate projections. We discuss the convergence properties of the new algorithm and also present numerical experiences obtained by applying it to image reconstruction problems using the SNARK93 system [3].
publishDate 2003
dc.date.none.fl_str_mv 2003-10
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info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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