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In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information…

系统与控制 · 电气工程与系统科学 2021-10-07 Suad Krilašević , Sergio Grammatico

In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose a novel continuous-time solution algorithm that uses regular projections and first-order information. As…

系统与控制 · 电气工程与系统科学 2020-07-23 Suad Krilašević , Sergio Grammatico

In this work, we investigate the distributed generalized Nash equilibrium (GNE) seeking problems for $N$-coalition games with inequality constraints. First, we study the scenario where each agent in a coalition has full information of all…

最优化与控制 · 数学 2021-09-28 Chao Sun , Guoqiang Hu

In this paper, the problem of finding a generalized Nash equilibrium (GNE) of a networked game is studied. Players are only able to choose their decisions from a feasible action set. The feasible set is considered to be a private linear…

计算机科学与博弈论 · 计算机科学 2017-03-27 Farzad Salehisadaghiani , Lacra Pavel

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…

最优化与控制 · 数学 2025-12-10 Stuart M. Harwood , Dimitri J. Papageorgiou

This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with merely monotone and restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method…

最优化与控制 · 数学 2025-07-18 Tatiana Tatarenko , Angelia Nedich

In this paper, we investigate distributed generalized Nash equilibrium (GNE) computation of monotone games with affine coupling constraints. Each player can only utilize its local objective function, local feasible set and a local block of…

最优化与控制 · 数学 2020-04-10 Peng Yi , Lacra Pavel

We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE seeking algorithm for monotone games that requires one…

最优化与控制 · 数学 2021-07-15 Barbara Franci , Sergio Grammatico

We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i.e., agent-wise) and global (i.e., shared resources) disturbances taking values in polyhedral uncertainty sets. By making…

系统与控制 · 电气工程与系统科学 2023-04-07 Marta Fochesato , Filippo Fabiani , John Lygeros

The multi-cluster games are addressed in this paper, where all players team up with the players in the cluster that they belong to, and compete against the players in other clusters to minimize the cost function of their own cluster. The…

系统与控制 · 电气工程与系统科学 2023-06-19 Zhenhua Deng , Yan Zhao

We design a distributed algorithm to seek generalized Nash equilibria of a robust game with uncertain coupled constraints. Due to the uncertainty of parameters in set constraints, we aim to find a generalized Nash equilibrium in the worst…

最优化与控制 · 数学 2022-04-05 Gehui Xu , Guanpu Chen , Hongsheng Qi

We design the first fully-distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direct access to the…

最优化与控制 · 数学 2022-06-16 Giuseppe Belgioioso , Angelia Nedić , Sergio Grammatico

Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet…

计算机科学与博弈论 · 计算机科学 2023-12-19 Hanyu Li , Wenhan Huang , Zhijian Duan , David Henry Mguni , Kun Shao , Jun Wang , Xiaotie Deng

In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used,…

机器人学 · 计算机科学 2025-11-07 Mark Pustilnik , Francesco Borrelli

In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the…

计算机科学与博弈论 · 计算机科学 2020-05-08 Carlo Cenedese , Giuseppe Belgioioso , Sergio Grammatico , Ming Cao

We consider the problem of computing a mixed-strategy generalized Nash equilibrium (MS-GNE) for a class of games where each agent has both continuous and integer decision variables. Specifically, we propose a novel Bregman…

最优化与控制 · 数学 2022-06-14 Wicak Ananduta , Sergio Grammatico

We study generalized games with full row rank equality constraints and we provide a strikingly simple proof of strong monotonicity of the associated KKT operator. This allows us to show linear convergence to a variational equilibrium of the…

最优化与控制 · 数学 2023-04-20 Mattia Bianchi , Emilio Benenati , Sergio Grammatico

In this paper, we consider a distributed Bayesian Nash equilibrium (BNE) seeking problem in incomplete-information aggregative games, which is a generalization of Bayesian games and deterministic aggregative games. We handle the aggregation…

最优化与控制 · 数学 2023-09-19 Hanzheng Zhang , Guanpu Chen , Huashu Qin

This paper proposes the first fully distributed algorithm for finding the Generalized Nash Equilibrium (GNE) of a non-cooperative game with shared coupling constraints and general cost coupling at a user-prescribed finite time T. As a…

最优化与控制 · 数学 2026-03-24 Liraz Mudrik , Isaac Kaminer , Sean Kragelund , Abram H. Clark

Distributed optimization and Nash equilibrium (NE) seeking problems have drawn much attention in the control community recently. This paper studies a class of non-cooperative games, known as N-cluster game, which subsumes both cooperative…

最优化与控制 · 数学 2023-03-01 Yipeng Pang , Guoqiang Hu
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