IntSat: integer linear programming by conflict-driven constraint learning

State-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article...

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Autores: Nieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138, Oliveras Llunell, Albert|||0000-0002-5893-1911, Rodríguez Carbonell, Enric|||0000-0003-1061-3954
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/397373
Acesso em linha:https://hdl.handle.net/2117/397373
https://dx.doi.org/10.1080/10556788.2023.2246167
Access Level:acceso abierto
Palavra-chave:Integer programming
Integer linear programming
SAT solving
Conflict-driven clause learning
Programació en nombres enters
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
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oai_identifier_str oai:upcommons.upc.edu:2117/397373
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repository_id_str
spelling IntSat: integer linear programming by conflict-driven constraint learningNieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138Oliveras Llunell, Albert|||0000-0002-5893-1911Rodríguez Carbonell, Enric|||0000-0003-1061-3954Integer programmingInteger linear programmingSAT solvingConflict-driven clause learningProgramació en nombres entersÀrees temàtiques de la UPC::Informàtica::Informàtica teòricaState-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article, we extend these techniques to Integer Linear Programming (ILP), where variables may take general integer values instead of purely binary ones, constraints are more expressive than just propositional clauses, and there may be an objective function to optimize. We explain how these methods can be implemented efficiently and discuss possible improvements. Our work is backed with a basic implementation showing that, even in this far less mature stage, our techniques are already a useful complement to the state of the art in ILP.All authors are supported by grant PID2021-122830OB-C43, funded by MCIN/ AEI/10.13039/501100011033 and by “ERDF: A way of making Europe”.Peer ReviewedTaylor & Francis Group20242024-01-0120232023-11-30journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/397373https://dx.doi.org/10.1080/10556788.2023.2246167reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3973732026-05-27T15:37:01Z
dc.title.none.fl_str_mv IntSat: integer linear programming by conflict-driven constraint learning
title IntSat: integer linear programming by conflict-driven constraint learning
spellingShingle IntSat: integer linear programming by conflict-driven constraint learning
Nieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138
Integer programming
Integer linear programming
SAT solving
Conflict-driven clause learning
Programació en nombres enters
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
title_short IntSat: integer linear programming by conflict-driven constraint learning
title_full IntSat: integer linear programming by conflict-driven constraint learning
title_fullStr IntSat: integer linear programming by conflict-driven constraint learning
title_full_unstemmed IntSat: integer linear programming by conflict-driven constraint learning
title_sort IntSat: integer linear programming by conflict-driven constraint learning
dc.creator.none.fl_str_mv Nieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138
Oliveras Llunell, Albert|||0000-0002-5893-1911
Rodríguez Carbonell, Enric|||0000-0003-1061-3954
author Nieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138
author_facet Nieuwenhuis, Robert Lukas Mario|||0000-0002-6489-2138
Oliveras Llunell, Albert|||0000-0002-5893-1911
Rodríguez Carbonell, Enric|||0000-0003-1061-3954
author_role author
author2 Oliveras Llunell, Albert|||0000-0002-5893-1911
Rodríguez Carbonell, Enric|||0000-0003-1061-3954
author2_role author
author
dc.subject.none.fl_str_mv Integer programming
Integer linear programming
SAT solving
Conflict-driven clause learning
Programació en nombres enters
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
topic Integer programming
Integer linear programming
SAT solving
Conflict-driven clause learning
Programació en nombres enters
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
description State-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article, we extend these techniques to Integer Linear Programming (ILP), where variables may take general integer values instead of purely binary ones, constraints are more expressive than just propositional clauses, and there may be an objective function to optimize. We explain how these methods can be implemented efficiently and discuss possible improvements. Our work is backed with a basic implementation showing that, even in this far less mature stage, our techniques are already a useful complement to the state of the art in ILP.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-30
2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/397373
https://dx.doi.org/10.1080/10556788.2023.2246167
url https://hdl.handle.net/2117/397373
https://dx.doi.org/10.1080/10556788.2023.2246167
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis Group
publisher.none.fl_str_mv Taylor & Francis Group
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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