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A Distributed Forward-Backward Algorithm for Stochastic Generalized Nash Equilibrium Seeking
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DOI：10.1109/TAC.2020.3047369.png)
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摘要
En 中文
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.
Keyword:
Cost function
Stochastic processes
Random variables
Nash equilibrium
Uncertainty
Convergence
Approximation algorithms
Stochastic approximation
stochastic generalized Nash equilibrium problems (SGNEPs)
variational inequalities
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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