论文标题
随机广义纳什平衡仅在单调游戏中
Stochastic generalized Nash equilibrium seeking in merely monotone games
论文作者
论文摘要
我们在仅具有预期价值成本功能的单调游戏中解决了随机通用的NASH平衡（SGNE）问题。具体而言，我们介绍了第一个分布式的SGNE寻求单调游戏算法，该算法需要一个近端计算（例如一个投影步骤）和一个pseudogradient评估。我们的主要贡献是将Malitsky（数学编程，2019年）的放松前向前后操作员分裂扩展到随机案例，进而将伪标的预期值近似于平均多个随机样品的平均值近似。
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 requires one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward-backward operator splitting by Malitsky (Mathematical Programming, 2019) to the stochastic case 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.
