DROPS

Document

DOI: 10.4230/LIPIcs.STACS.2023.20

Improved NP-Hardness of Approximation for Orthogonality Dimension and Minrank

Authors: Dror Chawin and Ishay Haviv

Published in: LIPIcs, Volume 254, 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023)

Abstract

The orthogonality dimension of a graph G over ℝ is the smallest integer k for which one can assign a nonzero k-dimensional real vector to each vertex of G, such that every two adjacent vertices receive orthogonal vectors. We prove that for every sufficiently large integer k, it is NP-hard to decide whether the orthogonality dimension of a given graph over ℝ is at most k or at least 2^{(1-o(1))⋅k/2}. We further prove such hardness results for the orthogonality dimension over finite fields as well as for the closely related minrank parameter, which is motivated by the index coding problem in information theory. This in particular implies that it is NP-hard to approximate these graph quantities to within any constant factor. Previously, the hardness of approximation was known to hold either assuming certain variants of the Unique Games Conjecture or for approximation factors smaller than 3/2. The proofs involve the concept of line digraphs and bounds on their orthogonality dimension and on the minrank of their complement.

Cite as

Dror Chawin and Ishay Haviv. Improved NP-Hardness of Approximation for Orthogonality Dimension and Minrank. In 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 254, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{chawin_et_al:LIPIcs.STACS.2023.20,
  author =	{Chawin, Dror and Haviv, Ishay},
  title =	{{Improved NP-Hardness of Approximation for Orthogonality Dimension and Minrank}},
  booktitle =	{40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023)},
  pages =	{20:1--20:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-266-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{254},
  editor =	{Berenbrink, Petra and Bouyer, Patricia and Dawar, Anuj and Kant\'{e}, Mamadou Moustapha},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2023.20},
  URN =		{urn:nbn:de:0030-drops-176724},
  doi =		{10.4230/LIPIcs.STACS.2023.20},
  annote =	{Keywords: hardness of approximation, graph coloring, orthogonality dimension, minrank, index coding}
}

Search Results

Documents authored by Chawin, Dror

Improved NP-Hardness of Approximation for Orthogonality Dimension and Minrank

Abstract

Cite as

Thanks for your feedback!

Could not send message