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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.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 .
dc.identifier.otherPURE: 250522992
dc.identifier.otherPURE UUID: 8a56ff34-e5bc-4dad-a3bc-c63d391de55e
dc.identifier.otherRIS: urn:7B45A791D32409E8AD08829360479802
dc.identifier.otherScopus: 85025834662
dc.identifier.otherORCID: /0000-0002-5052-8634/work/35084050
dc.identifier.otherORCID: /0000-0001-9519-938X/work/35084065
dc.identifier.otherWOS: 000411167600002
dc.identifier.otherORCID: /0000-0003-2979-5989/work/60887533
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281427
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.relation.ispartofArtificial Intelligenceen
dc.rights© 2017 Elsevier Ltd. All rights reserved. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectConstraint satisfactionen
dc.subjectCommon subexpression eliminationen
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.titleAutomatically improving constraint models in Savile Rowen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Interdisciplinary Research in Computational Algebraen
dc.description.statusPeer revieweden

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