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dc.contributor.authorSpracklen, Patrick
dc.contributor.authorDang, Nguyen
dc.contributor.authorAkgun, Ozgur
dc.contributor.authorMiguel, Ian James
dc.contributor.editorSchiex, Thomas
dc.contributor.editorde Givry, Simon
dc.identifier.citationSpracklen , P , Dang , N , Akgun , O & Miguel , I J 2019 , Automatic streamlining for constrained optimisation . in T Schiex & S de Givry (eds) , Principles and Practice of Constraint Programming : 25th International Conference, CP 2019, Stamford, CT, USA, September 30 – October 4, 2019, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11802 LNCS , Springer , Cham , pp. 366-383 , 25th International Conference on Principles and Practice of Constraint Programming (CP 2019) , Stamford , United States , 30/09/19 .
dc.identifier.otherPURE: 261625048
dc.identifier.otherPURE UUID: ce8510eb-25d3-4db4-8cd8-c3c7682edfdf
dc.identifier.otherScopus: 85075729489
dc.identifier.otherORCID: /0000-0001-9519-938X/work/63045245
dc.identifier.otherORCID: /0000-0002-2693-6953/work/63046310
dc.identifier.otherScopus: 85075729489
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281439
dc.identifier.otherWOS: 000560404200022
dc.descriptionFunding: UK EPSRC grant EP/P015638/1.en
dc.description.abstractAugmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, which trade completeness for potentially very significant reduction in search. Recently an automated approach has been proposed, which produces streamliners via a set of streamliner generation rules. This existing automated approach to streamliner generation has two key limitations. First, it outputs a single streamlined model. Second, the approach is limited to satisfaction problems. We remove both limitations by providing a method to produce automatically a portfolio of streamliners, each representing a different balance between three criteria: how aggressively the search space is reduced, the proportion of training instances for which the streamliner admitted at least one solution, and the average reduction in quality of the objective value versus the unstreamlined model. In support of our new method, we present an automated approach to training and test instance generation, and provide several approaches to the selection and application of the streamliners from the portfolio. Empirical results demonstrate drastic improvements both to the time required to find good solutions early and to prove optimality on three problem classes.
dc.relation.ispartofPrinciples and Practice of Constraint Programmingen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.rightsCopyright © 2019 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 programmingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectTheoretical Computer Scienceen
dc.subjectComputer Science(all)en
dc.titleAutomatic streamlining for constrained optimisationen
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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