Approximating Nash Social Welfare by Matching and Local Search

Jugal Garg, Edin Husić, Wenzheng Li, László A. Végh, Jan Vondrák

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For any >0, we give a simple, deterministic (4+)-approximation algorithm for the Nash social welfare (NSW) problem under submodular valuations. The previous best approximation factor was 380 via a randomized algorithm. We also consider the asymmetric variant of the problem, where the objective is to maximize the weighted geometric mean of agents' valuations, and give an (ω + 2 + )-approximation if the ratio between the largest weight and the average weight is at most ω. We also show that the 12-EFX envy-freeness property can be attained simultaneously with a constant-factor approximation. More precisely, we can find an allocation in polynomial time which is both 12-EFX and a (8+)-approximation to the symmetric NSW problem under submodular valuations. The previous best approximation factor under 12-EFX was linear in the number of agents.

Original languageEnglish (US)
Title of host publicationSTOC 2023 - Proceedings of the 55th Annual ACM Symposium on Theory of Computing
EditorsBarna Saha, Rocco A. Servedio
PublisherAssociation for Computing Machinery
Pages1298-1310
Number of pages13
ISBN (Electronic)9781450399135
DOIs
StatePublished - Jun 2 2023
Event55th Annual ACM Symposium on Theory of Computing, STOC 2023 - Orlando, United States
Duration: Jun 20 2023Jun 23 2023

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)0737-8017

Conference

Conference55th Annual ACM Symposium on Theory of Computing, STOC 2023
Country/TerritoryUnited States
CityOrlando
Period6/20/236/23/23

Keywords

  • Approximation Algorithms
  • Combinatorial Optimization
  • Envy-freeness
  • Fairness
  • Local Search
  • Nash Social Welfare

ASJC Scopus subject areas

  • Software

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