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Fully Distributed Primal-Dual Generalized Nash Equilibrium Seeking Algorithm Under Partial-Decision Information
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DOI：10.1109/TCNS.2025.3649096.png)
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Abstract
En 中文
This article studies the distributed generalized Nash equilibrium (GNE) seeking problem for generalized games in a partial-decision information scenario. The feasible decision sets of all players in the game are coupled together by shared coupling constraints, and each player does not have full access to their opponent's decisions. In these games, each player aims to minimize its own cost function, which depends on its own decisions and those of other players. Using variational equilibrium as an improved solution and leveraging the operator splitting technique, the problem is restated as seeking the zero of the sum of monotone operators. Then, we propose a fully distributed algorithm based on a new modification of the forward-backward operator splitting methods. It is proved that the designed algorithm generates a sequence that is shown to be convergent to the variational GNE with constant step sizes, without relying on restricted cocoercivity of the forward operator. Moreover, based on the designed operators and supported by theoretical analysis, the designed algorithm can achieve linear convergence under standard assumptions on the game mapping. Finally, the proposed algorithm is also verified via numerical experiments.
Keywords:
Games
Heuristic algorithms
Couplings
Linear programming
Distributed algorithms
Convergence
Nash equilibrium
Vectors
Standards
Nickel
Distributed algorithm
generalized Nash equilibrium (GNE)
operator splitting
Journal
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5
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1.6K
Citations:
5.8K
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Cited Papers