Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization

Detalhes bibliográficos
Autor(a) principal: Munera, Danny
Data de Publicação: 2015
Outros Autores: Diaz, Daniel, Abreu, Salvador, Rossi, Francesca, Saraswat, Vijay, Codognet, Philippe
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/17130
Resumo: Stable matching problems have several practical applications. If preference lists are truncated and contain ties, finding a stable matching with maximal size is computationally difficult. We address this problem using a local search technique, based on Adaptive Search and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods. Moreover, parallel versions (particularly versions with communication) improve performance so much that very large and hard instances can be solved quickly.
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spelling Solving Hard Stable Matching Problems via Local Search and Cooperative ParallelizationStable matching problems have several practical applications. If preference lists are truncated and contain ties, finding a stable matching with maximal size is computationally difficult. We address this problem using a local search technique, based on Adaptive Search and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods. Moreover, parallel versions (particularly versions with communication) improve performance so much that very large and hard instances can be solved quickly.AAAI2016-01-29T17:39:41Z2016-01-292015-02-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/17130http://hdl.handle.net/10174/17130porDanny Munera, Daniel Diaz, Salvador Abreu, Francesca Rossi, Vijay Saraswat, et al. Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization. 29th AAAI Conference on Artificial Intelligence, Jan 2015, Austin, TX, United States.https://hal-paris1.archives-ouvertes.fr/hal-01144214/documentndndspa@di.uevora.ptndndnd283Munera, DannyDiaz, DanielAbreu, SalvadorRossi, FrancescaSaraswat, VijayCodognet, Philippeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:04:26Zoai:dspace.uevora.pt:10174/17130Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:09:23.365309Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
title Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
spellingShingle Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
Munera, Danny
title_short Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
title_full Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
title_fullStr Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
title_full_unstemmed Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
title_sort Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization
author Munera, Danny
author_facet Munera, Danny
Diaz, Daniel
Abreu, Salvador
Rossi, Francesca
Saraswat, Vijay
Codognet, Philippe
author_role author
author2 Diaz, Daniel
Abreu, Salvador
Rossi, Francesca
Saraswat, Vijay
Codognet, Philippe
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Munera, Danny
Diaz, Daniel
Abreu, Salvador
Rossi, Francesca
Saraswat, Vijay
Codognet, Philippe
description Stable matching problems have several practical applications. If preference lists are truncated and contain ties, finding a stable matching with maximal size is computationally difficult. We address this problem using a local search technique, based on Adaptive Search and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods. Moreover, parallel versions (particularly versions with communication) improve performance so much that very large and hard instances can be solved quickly.
publishDate 2015
dc.date.none.fl_str_mv 2015-02-16T00:00:00Z
2016-01-29T17:39:41Z
2016-01-29
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/17130
http://hdl.handle.net/10174/17130
url http://hdl.handle.net/10174/17130
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Danny Munera, Daniel Diaz, Salvador Abreu, Francesca Rossi, Vijay Saraswat, et al. Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization. 29th AAAI Conference on Artificial Intelligence, Jan 2015, Austin, TX, United States.
https://hal-paris1.archives-ouvertes.fr/hal-01144214/document
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dc.publisher.none.fl_str_mv AAAI
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