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dc.contributor.authorAkgün, Özgür
dc.contributor.authorDang, Nguyen
dc.contributor.authorMiguel, Ian
dc.contributor.authorSalamon, András Z.
dc.contributor.authorSpracklen, Patrick
dc.contributor.authorStone, Christopher
dc.contributor.editorHebrard, Emmanuel
dc.contributor.editorMusliu, Nysret
dc.date.accessioned2020-10-30T17:30:02Z
dc.date.available2020-10-30T17:30:02Z
dc.date.issued2020
dc.identifier.citationAkgün , Ö , Dang , N , Miguel , I , Salamon , A Z , Spracklen , P & Stone , C 2020 , Discriminating instance generation from abstract specifications : a case study with CP and MIP . in E Hebrard & N Musliu (eds) , Integration of Constraint Programming, Artificial Intelligence, and Operations Research : 17th International Conference, CPAIOR 2020, Vienna, Austria, September 21–24, 2020, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12296 LNCS , Springer , Cham , pp. 41-51 , 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020 , Vienna, Online , Austria , 21/09/20 . https://doi.org/10.1007/978-3-030-58942-4_3en
dc.identifier.citationconferenceen
dc.identifier.isbn9783030589417
dc.identifier.isbn9783030589424
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 270822702
dc.identifier.otherPURE UUID: 04b6f971-6a20-4104-b26a-8a664d101d92
dc.identifier.otherScopus: 85092159328
dc.identifier.otherORCID: /0000-0001-9519-938X/work/82500934
dc.identifier.otherORCID: /0000-0002-1415-9712/work/82501077
dc.identifier.otherORCID: /0000-0002-2693-6953/work/82501098
dc.identifier.otherORCID: /0000-0002-6930-2686/work/82501147
dc.identifier.urihttp://hdl.handle.net/10023/20866
dc.descriptionThis work is supported by EPSRC grant EP/P015638/1 and used the Cirrus UK National Tier-2 HPC Service at EPCC funded by the University of Edinburgh and EPSRC (EP/P020267/1).en
dc.description.abstractWe extend automatic instance generation methods to allow cross-paradigm comparisons. We demonstrate that it is possible to completely automate the search for benchmark instances that help to discriminate between solvers. Our system starts from a high level human-provided problem specification, which is translated into a specification for valid instances. We use the automated algorithm configuration tool irace to search for instances, which are translated into inputs for both MIP and CP solvers by means of the Conjure, Savile Row, and MiniZinc tools. These instances are then solved by CPLEX and Chuffed, respectively. We constrain our search for instances by requiring them to exhibit a significant advantage for MIP over CP, or vice versa. Experimental results on four optimisation problem classes demonstrate the effectiveness of our method in identifying instances that highlight differences in performance of the two solvers.
dc.format.extent11
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofIntegration of Constraint Programming, Artificial Intelligence, and Operations Researchen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)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 https://doi.org/10.1007/978-3-030-58942-4_3.en
dc.subjectConstraint Programmingen
dc.subjectInstance generationen
dc.subjectMIPen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science(all)en
dc.subjectTheoretical Computer Scienceen
dc.subjectDASen
dc.subject.lccQA75en
dc.titleDiscriminating instance generation from abstract specifications : a case study with CP and MIPen
dc.typeConference itemen
dc.description.versionPostprinten
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.doihttps://doi.org/10.1007/978-3-030-58942-4_3


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