This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achi...This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.展开更多
Distributed Nash equilibrium seeking of aggregative games is investigated and a continuous-time algorithm is proposed.The algorithm is designed by virtue of projected gradient play dynamics and aggregation tracking dy...Distributed Nash equilibrium seeking of aggregative games is investigated and a continuous-time algorithm is proposed.The algorithm is designed by virtue of projected gradient play dynamics and aggregation tracking dynamics,and is applicable to games with constrained strategy sets and weight-balanced communication graphs.The key feature of our method is that the proposed projected dynamics achieves exponential convergence,whereas such convergence results are only obtained for non-projected dynamics in existing works on distributed optimization and equilibrium seeking.Numerical examples illustrate the effectiveness of our methods.展开更多
基金supported by the National Natural Science Foundation of China (NSFC)(62222308, 62173181, 62073171, 62221004)the Natural Science Foundation of Jiangsu Province (BK20200744, BK20220139)+3 种基金Jiangsu Specially-Appointed Professor (RK043STP19001)the Young Elite Scientists Sponsorship Program by CAST (2021QNRC001)1311 Talent Plan of Nanjing University of Posts and Telecommunicationsthe Fundamental Research Funds for the Central Universities (30920032203)。
文摘This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.
基金This work was partially supported by the National Natural Science Foundation of China under Grant 61903027,72171171,62003239Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100Shanghai Sailing Program under Grant 20YF1453000.
文摘Distributed Nash equilibrium seeking of aggregative games is investigated and a continuous-time algorithm is proposed.The algorithm is designed by virtue of projected gradient play dynamics and aggregation tracking dynamics,and is applicable to games with constrained strategy sets and weight-balanced communication graphs.The key feature of our method is that the proposed projected dynamics achieves exponential convergence,whereas such convergence results are only obtained for non-projected dynamics in existing works on distributed optimization and equilibrium seeking.Numerical examples illustrate the effectiveness of our methods.
