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dc.contributor.authorDavidson, Ewan
dc.contributor.authorAkgun, Ozgur
dc.contributor.authorEspasa Arxer, Joan
dc.contributor.authorNightingale, Peter
dc.contributor.editorSimonis, Helmut
dc.identifier.citationDavidson , E , Akgun , O , Espasa Arxer , J & Nightingale , P 2020 , Effective encodings of constraint programming models to SMT . in H Simonis (ed.) , Principles and Practice of Constraint Programming : 26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings . Lecture Notes in Computer Science (Programming and Software Engineering) , vol. 12333 LNCS , Springer , pp. 143-159 , 26th International Conference on Principles and Practice of Constraint Programming (CP 2020) , Louvain-la-Neuve , Belgium , 7/09/20 .
dc.identifier.otherPURE: 269726330
dc.identifier.otherPURE UUID: 5d418525-265e-4c21-8944-9a84ef9db61e
dc.identifier.otherORCID: /0000-0001-9519-938X/work/79226824
dc.identifier.otherScopus: 85091316156
dc.descriptionFunding: UK EPSRC grant EP/P015638/1.en
dc.description.abstractSatisfiability Modulo Theories (SMT) is a well-established methodology that generalises propositional satisfiability (SAT) by adding support for a variety of theories such as integer arithmetic and bit-vector operations. SMT solvers have made rapid progress in recent years. In part, the efficiency of modern SMT solvers derives from the use of specialised decision procedures for each theory. In this paper we explore how the Essence Prime constraint modelling language can be translated to the standard SMT-LIB language. We target four theories: bit-vectors (QF_BV), linear integer arithmetic (QF_LIA), non-linear integer arithmetic (QF_NIA), and integer difference logic (QF_IDL). The encodings are implemented in the constraint modelling tool Savile Row. In an extensive set of experiments, we compare our encodings for the four theories, showing some notable differences and complementary strengths. We also compare our new encodings to the existing work targeting SMT and SAT, and to a well-established learning CP solver. Our two proposed encodings targeting the theory of bit-vectors (QF_BV) both substantially outperform earlier work on encoding to QF_BV on a large and diverse set of problem classes.
dc.relation.ispartofPrinciples and Practice of Constraint Programmingen
dc.relation.ispartofseriesLecture Notes in Computer Science (Programming and Software Engineering)en
dc.rightsCopyright © 2020 Springer Nature Switzerland AG. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted 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 modellingen
dc.subjectAutomated reformulationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleEffective encodings of constraint programming models to SMTen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen

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