2 Search Results for "Høgemo, Svein"

Document
DOI: 10.4230/LIPIcs.IPEC.2025.31
Hamiltonicity Parameterized by Mim-Width Is (Indeed) Para-NP-Hard

Authors: Benjamin Bergougnoux and Lars Jaffke

Published in: LIPIcs, Volume 358, 20th International Symposium on Parameterized and Exact Computation (IPEC 2025)

Abstract
We prove that Hamiltonian Path and Hamiltonian Cycle are NP-hard on graphs of linear mim-width 26, even when a linear order of the input graph with mim-width 26 is provided together with input. This fills a gap left by a broken proof of the para-NP-hardness of Hamiltonicity problems parameterized by mim-width.
Cite as

Benjamin Bergougnoux and Lars Jaffke. Hamiltonicity Parameterized by Mim-Width Is (Indeed) Para-NP-Hard. In 20th International Symposium on Parameterized and Exact Computation (IPEC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 358, pp. 31:1-31:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bergougnoux_et_al:LIPIcs.IPEC.2025.31,
  author =	{Bergougnoux, Benjamin and Jaffke, Lars},
  title =	{{Hamiltonicity Parameterized by Mim-Width Is (Indeed) Para-NP-Hard}},
  booktitle =	{20th International Symposium on Parameterized and Exact Computation (IPEC 2025)},
  pages =	{31:1--31:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-407-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{358},
  editor =	{Agrawal, Akanksha and van Leeuwen, Erik Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2025.31},
  URN =		{urn:nbn:de:0030-drops-251631},
  doi =		{10.4230/LIPIcs.IPEC.2025.31},
  annote =	{Keywords: Hamiltonian Path, Hamiltonian Cycle, Mim-Width, Para-NP-Hardness}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2020.47
Hierarchical Clusterings of Unweighted Graphs

Authors: Svein Høgemo, Christophe Paul, and Jan Arne Telle

Published in: LIPIcs, Volume 170, 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)

Abstract
We study the complexity of finding an optimal hierarchical clustering of an unweighted similarity graph under the recently introduced Dasgupta objective function. We introduce a proof technique, called the normalization procedure, that takes any such clustering of a graph G and iteratively improves it until a desired target clustering of G is reached. We use this technique to show both a negative and a positive complexity result. Firstly, we show that in general the problem is NP-complete. Secondly, we consider min-well-behaved graphs, which are graphs H having the property that for any k the graph H^{(k)} being the join of k copies of H has an optimal hierarchical clustering that splits each copy of H in the same optimal way. To optimally cluster such a graph H^{(k)} we thus only need to optimally cluster the smaller graph H. Co-bipartite graphs are min-well-behaved, but otherwise they seem to be scarce. We use the normalization procedure to show that also the cycle on 6 vertices is min-well-behaved.
Cite as

Svein Høgemo, Christophe Paul, and Jan Arne Telle. Hierarchical Clusterings of Unweighted Graphs. In 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 170, pp. 47:1-47:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{hgemo_et_al:LIPIcs.MFCS.2020.47,
  author =	{H{\o}gemo, Svein and Paul, Christophe and Telle, Jan Arne},
  title =	{{Hierarchical Clusterings of Unweighted Graphs}},
  booktitle =	{45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)},
  pages =	{47:1--47:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-159-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{170},
  editor =	{Esparza, Javier and Kr\'{a}l', Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2020.47},
  URN =		{urn:nbn:de:0030-drops-127139},
  doi =		{10.4230/LIPIcs.MFCS.2020.47},
  annote =	{Keywords: Hierarchical Clustering}
}
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