A randomized inexact proximal best-response scheme for potential stochastic nash games

Jinlong Lei, Uday V. Shanbhag

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

7 Scopus citations

Abstract

This paper considers a stochastic potential game in which each player solves a parameterized stochastic convex optimization problem. We propose a randomized inexact best-response (BR) scheme to compute the Nash equilibrium (NE). In each iteration, while the other players keep their strategies invariant, a single player is randomly chosen to update its equilibrium strategy by computing an inexact proximal BR by solving a player-specific stochastic program since exact solutions are generally unavailable in finite time. By imposing suitable conditions on the inexactness sequences, we prove the almost sure (a.s.) convergence and mean convergence of the iterates generated by the scheme to an NE. Finally, we present some preliminary numerics on the problem of congestion control.

Original languageEnglish (US)
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1646-1651
Number of pages6
ISBN (Electronic)9781509028733
DOIs
StatePublished - Jun 28 2017
Event56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia
Duration: Dec 12 2017Dec 15 2017

Publication series

Name2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume2018-January

Other

Other56th IEEE Annual Conference on Decision and Control, CDC 2017
Country/TerritoryAustralia
CityMelbourne
Period12/12/1712/15/17

All Science Journal Classification (ASJC) codes

  • Decision Sciences (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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