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dc.contributor.authorFellows, Michael R.
dc.contributor.authorJaffke, Lars
dc.contributor.authorKirály, Aliz Izabella
dc.contributor.authorRosamond, Frances
dc.contributor.authorWeller, Mathias
dc.date.accessioned2020-04-06T09:54:47Z
dc.date.available2020-04-06T09:54:47Z
dc.date.issued2018
dc.PublishedFellows MR, Jaffke L, Király AI, Rosamond FA, Weller M. What Is Known About Vertex Cover Kernelization?. Lecture Notes in Computer Science (LNCS). 2018;11011eng
dc.identifier.issn1611-3349en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttps://hdl.handle.net/1956/21701
dc.description.abstractWe are pleased to dedicate this survey on kernelization of the Vertex Cover problem, to Professor Juraj Hromkovič on the occasion of his 60th birthday. The Vertex Cover problem is often referred to as the Drosophila of parameterized complexity. It enjoys a long history. New and worthy perspectives will always be demonstrated first with concrete results here. This survey discusses several research directions in Vertex Cover kernelization. The Barrier Degree of Vertex Cover is discussed. We have reduction rules that kernelize vertices of small degree, including in this paper new results that reduce graphs almost to minimum degree five. Can this process go on forever? What is the minimum vertex-degree barrier for polynomial-time kernelization? Assuming the ETH Hypothesis, there is a minimum degree barrier. The idea of automated kernelization is discussed. We here report the first experimental results of an AI-guided branching algorithm for Vertex Cover whose logic seems amenable for application in finding reduction rules to kernelize small-degree vertices. The survey highlights a central open problem in parameterized complexity. Happy Birthday, Juraj!en_US
dc.language.isoengeng
dc.publisherSpringeren_US
dc.titleWhat Is Known About Vertex Cover Kernelization?en_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2020-02-19T10:39:00Z
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2018 Springer Nature Switzerland AGen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-319-98355-4_19
dc.identifier.cristin1795770
dc.source.journalLecture Notes in Computer Science (LNCS)


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