Distributed Computation of Nash Equilibria for Monotone Aggregative Games via Iterative Regularization

Jinlong Lei, Uday V. Shanbhag, Jie Chen

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

Abstract

This work considers an aggregative game over time-varying graphs, where each player's cost function depends on its own strategy and the aggregate of its competitors' strategies. Though the aggregate is unknown to any given player, each player may interact with its neighbors to construct an estimate of the aggregate. We design a distributed iterative Tikhonov regularization method in which each player may independently choose its steplengths and regularization parameters while meeting some overall coordination requirements. Under a monotonicity assumption on the concatenated player-specific gradient map, we prove that the generated sequence converges to the least-norm Nash equilibrium (i.e., a Nash equilibrium with the smallest two-norm) and validate the proposed method on a networked Nash-Cournot equilibrium problem.

Original languageEnglish (US)
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2285-2290
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - Dec 14 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
CountryKorea, Republic of
CityVirtual, Jeju Island
Period12/14/2012/18/20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'Distributed Computation of Nash Equilibria for Monotone Aggregative Games via Iterative Regularization'. Together they form a unique fingerprint.

Cite this