Stochastic generalized Nash equilibrium seeking under partial-decision information
B. Franci (TU Delft - Team Sergio Grammatico)
S. Grammatico (TU Delft - Team Sergio Grammatico, TU Delft - Team Bart De Schutter)
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Abstract
We consider for the first time a stochastic generalized Nash equilibrium problem, i.e., with expected-value cost functions and joint feasibility constraints, under partial-decision information, meaning that the agents communicate only with some trusted neighbors. We propose several distributed algorithms for network games and aggregative games that we show being special instances of a preconditioned forward–backward splitting method. We prove that the algorithms converge to a generalized Nash equilibrium when the forward operator is restricted cocoercive by using the stochastic approximation scheme with variance reduction to estimate the expected value of the pseudogradient.
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