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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22894
Ver los metadatos del registro completo
| id |
SEDICI_233d2ffd23d9755c9f84a417b0402d09 |
|---|---|
| oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/22894 |
| network_acronym_str |
SEDICI |
| repository_id_str |
1329 |
| 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 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
| format |
conferenceObject |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/22894 |
| url |
http://sedici.unlp.edu.ar/handle/10915/22894 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
| eu_rights_str_mv |
openAccess |
| 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) |
| dc.format.none.fl_str_mv |
application/pdf 1603-1615 |
| dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
| reponame_str |
SEDICI (UNLP) |
| collection |
SEDICI (UNLP) |
| instname_str |
Universidad Nacional de La Plata |
| instacron_str |
UNLP |
| institution |
UNLP |
| repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
| repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
| _version_ |
1846782825583345664 |
| score |
12.982451 |