Networked Aggregative Games with Linear Convergence
- Resource Type
- Conference
- Authors
- Zhu, Rongping; Zhang, Jiaqi; You, Keyou
- Source
- 2021 60th IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2021 60th IEEE Conference on. :3381-3386 Dec, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Conferences
Aggregates
Games
Consensus algorithm
Nash equilibrium
Cost function
Communication networks
- Language
- ISSN
- 2576-2370
This paper considers a networked aggregative game (NAG) where the players are distributed over a communication network. By only communicating with a subset of players, the goal of each player in the NAG is to minimize an individual cost function that depends on its own action and the aggregate of all the players’ actions. To this end, we design a novel distributed algorithm that jointly exploits the ideas of the consensus algorithm and the conditional projection descent. Under strongly monotone assumption on the pseudo-gradient mapping, the proposed algorithm with fixed step-sizes is proved to converge linearly to the unique Nash equilibrium of the NAG. Then the theoretical results are validated by numerical experiments.