Stochastic generalized Nash equilibrium seeking in merely monotone games
Optimization and Control
2021-07-15 v5 Computer Science and Game Theory
Systems and Control
Systems and Control
Abstract
We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE seeking algorithm for monotone games that requires one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward-backward operator splitting by Malitsky (Mathematical Programming, 2019) to the stochastic case and in turn to show almost sure convergence to a SGNE when the expected value of the pseudogradient is approximated by the average over a number of random samples.
Cite
@article{arxiv.2002.08318,
title = {Stochastic generalized Nash equilibrium seeking in merely monotone games},
author = {Barbara Franci and Sergio Grammatico},
journal= {arXiv preprint arXiv:2002.08318},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:1912.04165