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dc.contributor.authorArchibald, Blair
dc.contributor.authorDunlop, Fraser
dc.contributor.authorHoffmann, Ruth
dc.contributor.authorMcCreesh, Ciaran
dc.contributor.authorProsser, Patrick
dc.contributor.authorTrimble, James
dc.contributor.editorRousseau, Louis-Martin
dc.contributor.editorStergiou, Kostas
dc.identifier.citationArchibald , B , Dunlop , F , Hoffmann , R , McCreesh , C , Prosser , P & Trimble , J 2019 , Sequential and parallel solution-biased search for subgraph algorithms . in L-M Rousseau & K Stergiou (eds) , Integration of Constraint Programming, Artificial Intelligence, and Operations Research : 16th International Conference, CPAIOR 2019, Thessaloniki, Greece, June 4–7, 2019, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11494 LNCS , Springer , Cham , pp. 20-38 , 16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019 , Thessaloniki , Greece , 4/06/19 .
dc.identifier.otherPURE: 259334091
dc.identifier.otherPURE UUID: 868634c4-0469-4326-8d5f-6f10a1b1d092
dc.identifier.otherScopus: 85066859617
dc.identifier.otherORCID: /0000-0002-1011-5894/work/58531618
dc.descriptionFunding: This work was supported by the Engineering and Physical Sciences Research Council (grant numbers EP/P026842/1, EP/M508056/1, and EP/N007565).en
dc.description.abstractThe current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call “solution-biased search”. By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.
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.rights© 2019, Springer Nature Switzerland AG. 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 as such may differ slightly from the final published version. The final published version of this work is available at
dc.subjectComputer Science(all)en
dc.subjectTheoretical Computer Scienceen
dc.titleSequential and parallel solution-biased search for subgraph algorithmsen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.St Andrews GAP Centreen
dc.contributor.institutionUniversity of St Andrews.Pure Mathematicsen

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