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dc.contributor.authorNightingale, Peter
dc.contributor.authorAkgün, Özgür
dc.contributor.authorGent, Ian P.
dc.contributor.authorJefferson, Christopher
dc.contributor.authorMiguel, Ian
dc.contributor.authorSpracklen, Patrick
dc.date.accessioned2018-07-12T23:34:16Z
dc.date.available2018-07-12T23:34:16Z
dc.date.issued2017-10
dc.identifier250522992
dc.identifier8a56ff34-e5bc-4dad-a3bc-c63d391de55e
dc.identifier85025834662
dc.identifier000411167600002
dc.identifier.citationNightingale , P , Akgün , Ö , Gent , I P , Jefferson , C , Miguel , I & Spracklen , P 2017 , ' Automatically improving constraint models in Savile Row ' , Artificial Intelligence , vol. 251 , pp. 35-61 . https://doi.org/10.1016/j.artint.2017.07.001en
dc.identifier.issn0004-3702
dc.identifier.otherRIS: urn:7B45A791D32409E8AD08829360479802
dc.identifier.otherORCID: /0000-0002-5052-8634/work/35084050
dc.identifier.otherORCID: /0000-0001-9519-938X/work/35084065
dc.identifier.otherORCID: /0000-0003-2979-5989/work/60887533
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281427
dc.identifier.urihttps://hdl.handle.net/10023/15338
dc.descriptionAuthors thank the EPSRC for funding this work through grants EP/H004092/1, EP/K015745/1, EP/M003728/1, and EP/P015638/1. In addition, Dr Jefferson is funded by a Royal Society University Research Fellowship.en
dc.description.abstractWhen solving a combinatorial problem using Constraint Programming (CP) or Satisfiability (SAT), modelling and formulation are vital and difficult tasks. Even an expert human may explore many alternatives in modelling a single problem. We make a number of contributions in the automated modelling and reformulation of constraint models. We study a range of automated reformulation techniques, finding combinations of techniques which perform particularly well together. We introduce and describe in detail a new algorithm, X-CSE, to perform Associative-Commutative Common Subexpression Elimination (AC-CSE) in constraint problems, significantly improving existing CSE techniques for associative and commutative operators such as +. We demonstrate that these reformulation techniques can be integrated in a single automated constraint modelling tool, called Savile Row, whose architecture we describe. We use Savile Row as an experimental testbed to evaluate each reformulation on a set of 50 problem classes, with 596 instances in total. Our recommended reformulations are well worthwhile even including overheads, especially on harder instances where solver time dominates. With a SAT solver we observed a geometric mean of 2.15 times speedup compared to a straightforward tailored model without recommended reformulations. Using a CP solver, we obtained a geometric mean of 5.96 times speedup for instances taking over 10 seconds to solve.
dc.format.extent27
dc.format.extent767378
dc.language.isoeng
dc.relation.ispartofArtificial Intelligenceen
dc.subjectConstraint satisfactionen
dc.subjectCommon subexpression eliminationen
dc.subjectModellingen
dc.subjectReformulationen
dc.subjectPropositional satisfiabilityen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectLanguage and Linguisticsen
dc.subjectArtificial Intelligenceen
dc.subjectLinguistics and Languageen
dc.subjectNDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject~DC~en
dc.subject.lccQA75en
dc.subject.lccQA76en
dc.titleAutomatically improving constraint models in Savile Rowen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.contributor.sponsorEPSRCen
dc.contributor.sponsorEPSRCen
dc.contributor.sponsorThe Royal Societyen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.identifier.doi10.1016/j.artint.2017.07.001
dc.description.statusPeer revieweden
dc.date.embargoedUntil2018-07-13
dc.identifier.grantnumberEP/H004092/1en
dc.identifier.grantnumberEP/K015745/1en
dc.identifier.grantnumberEP/M003728/1en
dc.identifier.grantnumberUF1204070en


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