Stochastic generalized Nash equilibrium seeking in merely monotone games

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 only requires one proximal computation (e.g., one projection step) and only one pseudogradient evaluation per iteration. Our main contribution is to build upon the relaxed forward-backward operator splitting by Malitsky (Mathematical programming, 2019) 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.