We consider distributed computation of generalized Nash equilibrium (GNE) over networks, in games with shared coupling constraints. Existing methods require that each player has full access to opponents’ decisions. In this paper, we assume that players have only partial-decision information, and can communicate with their neighbors over an arbitrary undirected graph. We recast the problem as that of finding a zero of a sum of monotone operators through primal-dual analysis. To distribute the problem, we doubly augment variables, so that each player has local decision estimates and local copies of Lagrangian multipliers. We introduce a single-layer algorithm, fully distributed with respect to both primal and dual variables. We show its convergence to a variational GNE with fixed step sizes, by reformulating it as a forward–backward iteration for a pair of doubly-augmented monotone operators.

中文翻译：

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通过双增强算子分裂方法在网络上的部分决策信息下寻找分布式 GNE

我们考虑在具有共享耦合约束的游戏中通过网络对广义纳什均衡 (GNE) 进行分布式计算。现有方法要求每个玩家都可以完全了解对手的决定。在本文中，我们假设玩家只有部分决策信息，并且可以通过任意无向图与邻居进行通信。我们将问题重新定义为通过原始对偶分析找到单调算子和的零的问题。为了分布问题，我们对变量进行了双重扩充，以便每个参与者都有本地决策估计和拉格朗日乘数的本地副本。我们引入了一种单层算法，对于原始变量和对偶变量都是完全分布的。我们展示了它对具有固定步长的变分 GNE 的收敛，