论文标题
连续游戏的线性最后一卷收敛，并带有不等式约束
Linear Last-Iterate Convergence for Continuous Games with Coupled Inequality Constraints
论文作者
论文摘要
在本文中，在部分决策信息方案中研究了具有耦合仿射不平等限制的连续游戏的广义NASH平衡（GNE），以寻求问题，尽管其成本功能可能取决于所有其他玩家的策略，但每个玩家只能通过本地通信访问其邻居的信息。为此，设计了一种基于共识和双扩散方法的新型分散的原始二重算法，以寻求研究游戏的变化GNE。本文还提供了理论分析，以表明该设计算法对最后近期的算法收敛，据我们所知，该算法是第一个提出线性收敛的gne寻求算法在耦合仿射不平等约束下的算法。最后，提出了一个数值示例，以证明获得的理论结果的有效性。
In this paper, the generalized Nash equilibrium (GNE) seeking problem for continuous games with coupled affine inequality constraints is investigated in a partial-decision information scenario, where each player can only access its neighbors' information through local communication although its cost function possibly depends on all other players' strategies. To this end, a novel decentralized primal-dual algorithm based on consensus and dual diffusion methods is devised for seeking the variational GNE of the studied games. This paper also provides theoretical analysis to show that the designed algorithm converges linearly for the last-iterate, which, to our best knowledge, is the first to propose a linearly convergent GNE seeking algorithm under coupled affine inequality constraints. Finally, a numerical example is presented to demonstrate the effectiveness of the obtained theoretical results.
