A relaxed-inertial forward-backward-forward algorithm for stochastic generalized Nash equilibrium seeking
A relaxed-inertial forward-backward-forward algorithm for stochastic generalized Nash equilibrium seeking
复制标题
随机广义纳什均衡寻求的松弛惯性前向-后向-前向算法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Mathias Staudigl
中科院分区：
文献类型：
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作者：
Shisheng Cui;Barbara Franci;Sergio Grammatico;U. Shanbhag;Mathias Staudigl
提出了一种新的求解随机不确定性下分布式纳什均衡的算子分裂算法，该算法具有松弛和惯性效应。所提出的算法是由求解具有Lipschitz连续单调伪梯度算子的结构单调包含问题的一个向前-向后-向前格式导出的。据我们所知，这是第一个分布式的广义纳什均衡寻求算法，具有加速技术的随机纳什均衡问题，而不假设cocoercivity。数值例子说明了惯性和松弛对我们所提出的算法的性能的影响。
We propose a new operator splitting algorithm for distributed Nash equilibrium seeking under stochastic uncertainty, featuring relaxation and inertial effects. The proposed algorithm is derived from a forward-backward-forward scheme for solving structured monotone inclusion problems with Lipschitz continuous and monotone pseudogradient operator. To the best of our knowledge, this is the first distributed generalized Nash equilibrium seeking algorithm featuring acceleration techniques in stochastic Nash equilibrium problems without assuming cocoercivity. Numerical examples illustrate the effect of inertia and relaxation on the performance of our proposed algorithm.