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Main Authors: Liu, Zhaocong, Huang, Jie
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2405.07576
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author Liu, Zhaocong
Huang, Jie
author_facet Liu, Zhaocong
Huang, Jie
contents The problem of the distributed Nash equilibrium seeking for aggregative games has been studied over strongly connected and weight-balanced static networks and every time strongly connected and weight-balanced switching networks. In this paper, we further study the same problem over jointly connected and weight-balanced networks. The existing approaches critically rely on the connectedness of the network for constructing a Lyapunov function for their algorithms and theses approaches fail if the network is not connected. To overcome this difficulty, we propose an approach to show the exponential convergence of the output of the closed-loop system to the unknown Nash equilibrium (NE) point under a set of mild conditions.
format Preprint
id arxiv_https___arxiv_org_abs_2405_07576
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Distributed Nash Equilibrium Seeking in Aggregative Games over Jointly Connected and Weight-Balanced Networks
Liu, Zhaocong
Huang, Jie
Optimization and Control
The problem of the distributed Nash equilibrium seeking for aggregative games has been studied over strongly connected and weight-balanced static networks and every time strongly connected and weight-balanced switching networks. In this paper, we further study the same problem over jointly connected and weight-balanced networks. The existing approaches critically rely on the connectedness of the network for constructing a Lyapunov function for their algorithms and theses approaches fail if the network is not connected. To overcome this difficulty, we propose an approach to show the exponential convergence of the output of the closed-loop system to the unknown Nash equilibrium (NE) point under a set of mild conditions.
title Distributed Nash Equilibrium Seeking in Aggregative Games over Jointly Connected and Weight-Balanced Networks
topic Optimization and Control
url https://arxiv.org/abs/2405.07576