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dc.contributor.authorBaste, Julien
dc.contributor.authorFellows, Michael Ralph
dc.contributor.authorJaffke, Lars
dc.contributor.authorMasařík, Tomáš
dc.contributor.authorOliveira, Mateus De Oliveira
dc.contributor.authorPhilip, Geevarghese
dc.contributor.authorRosamond, Frances
dc.date.accessioned2023-01-04T13:29:57Z
dc.date.available2023-01-04T13:29:57Z
dc.date.created2022-05-06T14:17:36Z
dc.date.issued2022
dc.identifier.issn0004-3702
dc.identifier.urihttps://hdl.handle.net/11250/3040978
dc.description.abstractWhen modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that instance, but in finding a sufficiently diverse collection of good solutions. In this work we initiate a systematic study of diversity from the point of view of fixed-parameter tractability theory. First, we consider an intuitive notion of diversity of a collection of solutions which suits a large variety of combinatorial problems of practical interest. We then present an algorithmic framework which –automatically– converts a tree-decomposition-based dynamic programming algorithm for a given combinatorial problem X into a dynamic programming algorithm for the diverse version of X. Surprisingly, our algorithm has a polynomial dependence on the diversity parameter.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleDiversity of solutions: An exploration through the lens of fixed-parameter tractability theoryen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2022 Elsevieren_US
dc.source.articlenumber103644en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doi10.1016/j.artint.2021.103644
dc.identifier.cristin2022172
dc.source.journalArtificial Intelligenceen_US
dc.relation.projectNorges forskningsråd: 326537en_US
dc.relation.projectNorges forskningsråd: 288761en_US
dc.identifier.citationArtificial Intelligence. 2022, 303, 103644.en_US
dc.source.volume303en_US


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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