Distributed Nash Equilibrium Learning for Average Aggregative Games: Harnessing Smoothness to Accelerate the Algorithm-Reference-Cited by-同舟云学术

Distributed Nash Equilibrium Learning for Average Aggregative Games: Harnessing Smoothness to Accelerate the Algorithm

Published:2023-09 Issue:3 Volume:17 Page:4855-4865
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Pan Wei¹^ORCID,Xu Xinli²,Lu Yu³,Zhang Weidong¹^ORCID

Affiliation:

1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

2. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

3. School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

Key R&D Program of Guangdong

Key R&D Program of Hainan

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10235272/10109500.pdf?arnumber=10109500

Reference44 articles.

1. Distributed Process Monitoring for Multi-Agent Systems Through Cognitive Learning

2. Harnessing Smoothness to Accelerate Distributed Optimization

3. Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

4. Generalized Nash equilibrium seeking strategy for distributed nonsmooth multi-cluster game

5. Reduced-Order Nonlinear Observers via Contraction Analysis and Convex Optimization

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