A distributed primal-dual algorithm for computation of generalized Nash equilibria via operator splitting methods
A distributed primal-dual algorithm for computation of generalized Nash equilibria via operator splitting methods
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DOI：
10.1109/cdc.2017.8264224
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发表时间：
2017-12
期刊：
影响因子：
--
通讯作者：
Peng Yi;Lacra Pavel
中科院分区：
文献类型：
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作者：
Peng Yi;Lacra Pavel
本文提出了一种计算网络上非合作博弈广义纳什均衡的分布式算法。我们考虑这样一种对策，其中所有参与者的可行决策集通过全局共享仿射约束耦合在一起。采用变分GNOE作为精化解，通过原始-对偶分析和变量增广，将问题转化为求单调算子和的零点问题。然后介绍了一种基于正反向算子分裂方法的分布式算法。每个玩家只需要知道自己的局部目标函数、局部可行集和仿射约束的局部块，并与邻居共享信息。我们证明了该算法在固定步长的情况下是收敛的。给出了有限市场容量下网络古诺竞争的数值模拟。
In this paper, we propose a distributed algorithm for computation of a generalized Nash equilibrium (GNE) in noncooperative games over networks. We consider games in which the feasible decision sets of all players are coupled together by a globally shared affine constraint. Adopting the variational GNE as a refined solution, we reformulate the problem as that of finding the zeros of a sum of monotone operators through a primal-dual analysis and an augmentation of variables. Then we introduce a distributed algorithm based on forward-backward operator splitting methods. Each player only needs to know its local objective function, local feasible set, and a local block of the affine constraint, and share information with its neighbours. We show convergence of the proposed algorithm for fixed step-sizes. Numerical simulations are given for networked Cournot competition with bounded market capacities.