Approximate Nash Equilibria in Large Nonconvex Aggregative Games

Author:

Liu Kang12ORCID,Oudjane Nadia34ORCID,Wan Cheng34ORCID

Affiliation:

1. Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128 Palaiseau Cedex, France;

2. Inria Saclay Center, 91120 Palaiseau, France;

3. Research & Development, EDF Lab Paris-Saclay, 91120 Palaiseau, France;

4. Finance for Energy Market Research Centre, France

Abstract

This paper shows the existence of [Formula: see text]-Nash equilibria in n-player noncooperative sum-aggregative games in which the players’ cost functions, depending only on their own action and the average of all players’ actions, are lower semicontinuous in the former, whereas γ-Hölder continuous in the latter. Neither the action sets nor the cost functions need to be convex. For an important class of sum-aggregative games, which includes congestion games with γ equal to one, a gradient-proximal algorithm is used to construct [Formula: see text]-Nash equilibria with at most [Formula: see text] iterations. These results are applied to a numerical example concerning the demand-side management of an electricity system. The asymptotic performance of the algorithm when n tends to infinity is illustrated.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications,General Mathematics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Continuous-Time Damping-Based Mirror Descent for a Class of Non-Convex Multi-Player Games with Coupling Constraints;2024 IEEE 18th International Conference on Control & Automation (ICCA);2024-06-18

2. Stackelberg and Nash Equilibrium Computation in Non-Convex Leader-Follower Network Aggregative Games;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-02

3. Achieving the Social Optimum in a Nonconvex Cooperative Aggregative Game: A Distributed Stochastic Annealing Approach;IEEE Transactions on Neural Networks and Learning Systems;2024

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