Files in this item
Automated streamliner portfolios for constraint satisfaction problems
Item metadata
dc.contributor.author | Spracklen, Justin Lewis Patrick John | |
dc.contributor.author | Dang, Nguyen | |
dc.contributor.author | Akgun, Ozgur | |
dc.contributor.author | Miguel, Ian James | |
dc.date.accessioned | 2023-04-06T12:30:09Z | |
dc.date.available | 2023-04-06T12:30:09Z | |
dc.date.issued | 2023-06-01 | |
dc.identifier | 283948213 | |
dc.identifier | c5cc8a3b-23e0-49a1-8d94-d62e720c5edb | |
dc.identifier | 85151800100 | |
dc.identifier.citation | Spracklen , J L P J , Dang , N , Akgun , O & Miguel , I J 2023 , ' Automated streamliner portfolios for constraint satisfaction problems ' , Artificial Intelligence , vol. 319 , 103915 . https://doi.org/10.1016/j.artint.2023.103915 | en |
dc.identifier.issn | 0004-3702 | |
dc.identifier.other | ORCID: /0000-0002-6930-2686/work/132214059 | |
dc.identifier.other | ORCID: /0000-0001-9519-938X/work/132214162 | |
dc.identifier.other | ORCID: /0000-0002-2693-6953/work/132214312 | |
dc.identifier.uri | https://hdl.handle.net/10023/27357 | |
dc.description | Funding: 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 (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1). | en |
dc.description.abstract | Constraint 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.format.extent | 24 | |
dc.format.extent | 4144396 | |
dc.language.iso | eng | |
dc.relation.ispartof | Artificial Intelligence | en |
dc.subject | Constraint programming | en |
dc.subject | Constraint modelling | en |
dc.subject | Constraint satisfaction problem | en |
dc.subject | Algorithm selection | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | 3rd-DAS | en |
dc.subject | MCC | en |
dc.subject.lcc | QA75 | en |
dc.title | Automated streamliner portfolios for constraint satisfaction problems | en |
dc.type | Journal article | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.sponsor | The Leverhulme Trust | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. Centre for Interdisciplinary Research in Computational Algebra | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.identifier.doi | 10.1016/j.artint.2023.103915 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | EP/P026842/1 | en |
dc.identifier.grantnumber | ECF-2020-168 | en |
This item appears in the following Collection(s)
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.