License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022.43
URN: urn:nbn:de:0030-drops-171656
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/17165/
Go to the corresponding LIPIcs Volume Portal

Ghoshal, Suprovat ; Louis, Anand

Approximating CSPs with Outliers

pdf-format:
LIPIcs-APPROX43.pdf (0.7 MB)

Abstract

Constraint satisfaction problems (CSPs) are ubiquitous in theoretical computer science. We study the problem of Strong-CSP s, i.e. instances where a large induced sub-instance has a satisfying assignment. More formally, given a CSP instance ?(V, E, [k], {Π_{ij}}_{(i,j) ∈ E}) consisting of a set of vertices V, a set of edges E, alphabet [k], a constraint Π_{ij} ⊂ [k] × [k] for each (i,j) ∈ E, the goal of this problem is to compute the largest subset S ⊆ V such that the instance induced on S has an assignment that satisfies all the constraints.
In this paper, we study approximation algorithms for UniqueGames and related problems under the Strong-CSP framework when the underlying constraint graph satisfies mild expansion properties. In particular, we show that given a StrongUniqueGames instance whose optimal solution S^* is supported on a regular low threshold rank graph, there exists an algorithm that runs in time exponential in the threshold rank, and recovers a large satisfiable sub-instance whose size is independent on the label set size and maximum degree of the graph. Our algorithm combines the techniques of Barak-Raghavendra-Steurer (FOCS'11), Guruswami-Sinop (FOCS'11) with several new ideas and runs in time exponential in the threshold rank of the optimal set. A key component of our algorithm is a new threshold rank based spectral decomposition, which is used to compute a "large" induced subgraph of "small" threshold rank; our techniques build on the work of Oveis Gharan and Rezaei (SODA'17), and could be of independent interest.

BibTeX - Entry

@InProceedings{ghoshal_et_al:LIPIcs.APPROX/RANDOM.2022.43,
  author =	{Ghoshal, Suprovat and Louis, Anand},
  title =	{{Approximating CSPs with Outliers}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
  pages =	{43:1--43:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-249-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{245},
  editor =	{Chakrabarti, Amit and Swamy, Chaitanya},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/17165},
  URN =		{urn:nbn:de:0030-drops-171656},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2022.43},
  annote =	{Keywords: Constraint Satisfaction Problems, Strong Unique Games, Threshold Rank}
}

Keywords: Constraint Satisfaction Problems, Strong Unique Games, Threshold Rank
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)
Issue Date: 2022
Date of publication: 15.09.2022

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI