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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorBodlaender, Hans
dc.contributor.authorJong, Jack de
dc.date.accessioned2024-03-02T01:00:58Z
dc.date.available2024-03-02T01:00:58Z
dc.date.issued2024
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/46106
dc.description.abstractIn the field of parameterised complexity, there has been a significant amount of research into the parameters treewidth and pathwidth, but not a comparable amount of research into the related tree-depth parameter. In this paper, we try to expand our knowledge about the hardness of a set of ‘flow-like’ graph problems when parameterised by tree-depth, similar to work done in [1] and [2] where the same set of problems was considered for pathwidth and treewidth respectively. We also provide hardness proofs for the class XSLP, which was defined in [3] by Bodlaender et al., and is intended to serve as a ‘natural home’ for problems parameterised by tree-depth, similar to how XALP and XNLP are intended as ‘natural homes’ for problems parameterised by treewidth and foor pathwidth respectively. Furthermore, by showing that a parameterised reduction exists between any two problems in the set of flow-like problems we consider when using the tree-depth parameter, we support a conjecture that this set of problems is in a different complexity class that is distinct from XSLP.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectIn this paper, we try to expand our knowledge about the hardness of a set of ‘flow-like’ graph problems when parameterised by tree-depth, and we also show why they are probably not complete for the complexity class XSLP. We also provide several XSLP-hardness proofs for a set of graph problems when parameterised by logarithmic tree-depth.
dc.titleAnalysing flow-like problems parameterised by tree-depth
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsParameterized Complexity; XSLP; Tree-depth; Binary CSP ; Logarithmic Tree-depth
dc.subject.courseuuComputing Science
dc.thesis.id28782


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