Distributed Heavy-Ball Nash Equilibrium Seeking Algorithm in Aggregative Games

In this paper, we address the distributed Nash equilibrium seeking problem in an aggregative game, in which each agent is required to optimize a self-interested objective function that depends on both its own decision and the aggregate of all agents' decisions. By integrating the heavy-ball method with consensus-based gradient method, a novel distributed algorithm is proposed for seeking the Nash equilibrium with an improved convergence rate. Rigorous theoretical analysis is provided to prove the convergence of the algorithm. Finally, detailed numerical simulation results are provided to show the effectiveness and the acceleration performance of our algorithm.

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