A Distributed Forward–Backward Algorithm for Stochastic Generalized Nash Equilibrium Seeking
Barbara Franci, Sergio Grammatico
Abstract
We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward–backward operator splitting for SGNEPs, where, at each iteration, the expected value of the pseudogradient is approximated via a number of random samples. Our main contribution is to show almost sure convergence of the proposed algorithm if the pseudogradient mapping is restricted (monotone and) cocoercive.
Topics & Concepts
Nash equilibriumConvergence (economics)Monotone polygonMathematicsMathematical optimizationOperator (biology)Applied mathematicsValue (mathematics)AlgorithmMathematical economicsStatisticsRepressorEconomicsGeneBiochemistryGeometryEconomic growthChemistryTranscription factorOptimization and Variational AnalysisAdaptive Dynamic Programming ControlDistributed Control Multi-Agent Systems