An Optimal Distributed Algorithm with Operator Extrapolation for Stochastic Aggregative Games

TL;DR

This paper introduces a distributed operator extrapolation algorithm for stochastic aggregative games that achieves the optimal convergence rate of (1/k) for Nash equilibrium seeking under strong monotonicity assumptions.

Contribution

It proposes a novel distributed algorithm with operator extrapolation that accelerates convergence in stochastic aggregative games, achieving optimal rates.

Findings

Achieves (1/k) convergence rate under strong monotonicity.

Demonstrates effectiveness through numerical simulations.

Utilizes operator extrapolation to leverage historical gradient information.

Abstract

This work studies Nash equilibrium seeking for a class of stochastic aggregative games, where each player has an expectation-valued objective function depending on its local strategy and the aggregate of all players' strategies. We propose a distributed algorithm with operator extrapolation, in which each player maintains an estimate of this aggregate by exchanging this information with its neighbors over a time-varying network, and updates its decision through the mirror descent method. An operator extrapolation at the search direction is applied such that the two step historical gradient samples are utilized to accelerate the convergence. Under the strongly monotone assumption on the pseudo-gradient mapping, we prove that the proposed algorithm can achieve the optimal convergence rate of $\mathcal{O}(1/k)$ for Nash equilibrium seeking of stochastic games. Finally, the algorithm performance is demonstrated via numerical simulations.