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dc.contributor.authorSpracklen, Justin Lewis Patrick John
dc.contributor.authorDang, Nguyen
dc.contributor.authorAkgun, Ozgur
dc.contributor.authorMiguel, Ian James
dc.identifier.citationSpracklen , J L P J , Dang , N , Akgun , O & Miguel , I J 2023 , ' Automated streamliner portfolios for constraint satisfaction problems ' , Artificial Intelligence , vol. 319 , 103915 .
dc.identifier.otherORCID: /0000-0002-6930-2686/work/132214059
dc.identifier.otherORCID: /0000-0001-9519-938X/work/132214162
dc.identifier.otherORCID: /0000-0002-2693-6953/work/132214312
dc.descriptionFunding: This work is supported by the EPSRC grants EP/P015638/1 and EP/P026842/1, and Nguyen Dang is a Leverhulme Early Career Fellow. The authors used the Cirrus UK National Tier-2 HPC Service at EPCC ( funded by the University of Edinburgh and EPSRC (EP/P020267/1).en
dc.description.abstractConstraint Programming (CP) is a powerful technique for solving large-scale combinatorial problems. Solving a problem proceeds in two distinct phases: modelling and solving. Effective modelling has a huge impact on the performance of the solving process. Even with the advance of modern automated modelling tools, search spaces involved can be so vast that problems can still be difficult to solve. To further constrain the model, a more aggressive step that can be taken is the addition of streamliner constraints, which are not guaranteed to be sound but are designed to focus effort on a highly restricted but promising portion of the search space. Previously, producing effective streamlined models was a manual, difficult and time-consuming task. This paper presents a completely automated process to the generation, search and selection of streamliner portfolios to produce a substantial reduction in search effort across a diverse range of problems. The results demonstrate a marked improvement in performance for both Chuffed, a CP solver with clause learning, and lingeling, a modern SAT solver.
dc.relation.ispartofArtificial Intelligenceen
dc.subjectConstraint programmingen
dc.subjectConstraint modellingen
dc.subjectConstraint satisfaction problemen
dc.subjectAlgorithm selectionen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleAutomated streamliner portfolios for constraint satisfaction problemsen
dc.typeJournal articleen
dc.contributor.sponsorThe Leverhulme Trusten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.description.statusPeer revieweden

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