Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games

In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where b...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 49; H. 6; S. 2362 - 2371
Hauptverfasser:	Lu, Kaihong, Jing, Gangshan, Wang, Long
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Aggregates Computer simulation Consensus Cost function distributed algorithm Distributed algorithms Economic models Game theory Games Linear equations Mathematical model Nash equilibrium Nash equilibrium (NE) noncooperative game Nonlinear equations Search algorithms Search problems
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players' actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.
AbstractList	In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players' actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results. In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players' actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players' actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.
Author	Wang, Long Lu, Kaihong Jing, Gangshan
Author_xml	– sequence: 1 givenname: Kaihong surname: Lu fullname: Lu, Kaihong email: khong_lu@163.com organization: Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi'an, China – sequence: 2 givenname: Gangshan surname: Jing fullname: Jing, Gangshan email: nameisjing@gmail.com organization: Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi'an, China – sequence: 3 givenname: Long surname: Wang fullname: Wang, Long email: longwang@pku.edu.cn organization: Center for Systems and Control, College of Engineering, Peking University, Beijing, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29994016$$D View this record in MEDLINE/PubMed
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SubjectTerms	Aggregates Computer simulation Consensus Cost function distributed algorithm Distributed algorithms Economic models Game theory Games Linear equations Mathematical model Nash equilibrium Nash equilibrium (NE) noncooperative game Nonlinear equations Search algorithms Search problems
Title	Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games
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