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Handling Correlated Rounding Error via Preclustering: A 1.73-approximation for Correlation Clustering

  • Vincent Cohen-Addad
  • , Euiwoong Lee
  • , Shi Li
  • , Alantha Newman
  • Google Research
  • University of Michigan, Ann Arbor
  • Université Grenoble Alpes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

We consider the classic correlation clustering problem: Given a complete graph where edges are labelled either + or -, the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges within parts. Recently, Cohen-Addad, Lee and Newman [CLN22] gave a 1.995-approximation for the problem using the Sherali-Adams hierarchy, hence beating the integrality gap of 2 of the classic linear program. We significantly improve upon this result by providing a 1.73-approximation for the problem. Our approach brings together a new preprocessing of correlation clustering instances that enables a new LP formulation which combined with the algorithm from [CLN22] yields the improved bound.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 64th Annual Symposium on Foundations of Computer Science, FOCS 2023
PublisherIEEE Computer Society
Pages1082-1104
Number of pages23
ISBN (Electronic)9798350318944
DOIs
StatePublished - 2023
Event64th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2023 - Santa Cruz, United States
Duration: Nov 6 2023Nov 9 2023

Publication series

NameProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
ISSN (Print)0272-5428

Conference

Conference64th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2023
Country/TerritoryUnited States
CitySanta Cruz
Period11/6/2311/9/23

Keywords

  • LP-hierarchies
  • Sherali-Adams
  • approximation algorithms
  • clustering

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