Scholarlink

<p>Generalized Nash equilibrium (GNE) is a solution concept for complete information games, in which each player’s objective function and feasible region depend on other players’ actions. While numerical methods for finding GNE when players possess convex structure are relatively mature, the same cannot be said when players optimize nonconvex objective functions over nonconvex feasible regions. Drawing inspiration from the notion of a normalized (or variational) Nash equilibrium, which is a more restrictive class of solutions to generalized Nash games, we extend the ideas of Harwood et al. (Comput Optim Appl 87(2):641–676, 2024) to develop an exact method that can find a normalized Nash equilibrium (NNE) of a problem, when such an NNE exists. By adapting the framework of Harwood et al. (Comput Optim Appl 87(2):641–676, 2024), we are able to find NNE without any convexity assumptions. We demonstrate the effectiveness of our method on several nonconvex games.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Computing normalized Nash equilibria for generalized Nash games with nonconvex players

  • 期刊论文

  • 发表日期：

    2025年12月8日

查看全文
  • Stuart M. Harwood,
  • Dimitri J. Papageorgiou

摘要

Generalized Nash equilibrium (GNE) is a solution concept for complete information games, in which each player’s objective function and feasible region depend on other players’ actions. While numerical methods for finding GNE when players possess convex structure are relatively mature, the same cannot be said when players optimize nonconvex objective functions over nonconvex feasible regions. Drawing inspiration from the notion of a normalized (or variational) Nash equilibrium, which is a more restrictive class of solutions to generalized Nash games, we extend the ideas of Harwood et al. (Comput Optim Appl 87(2):641–676, 2024) to develop an exact method that can find a normalized Nash equilibrium (NNE) of a problem, when such an NNE exists. By adapting the framework of Harwood et al. (Comput Optim Appl 87(2):641–676, 2024), we are able to find NNE without any convexity assumptions. We demonstrate the effectiveness of our method on several nonconvex games.