Distributed generalized Nash equilibrium seeking in aggregative games on time-varying networks

Abstract

We design the first fully-distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direct access to the aggregate decision. The algorithm is derived by integrating dynamic tracking into a projected pseudo-gradient algorithm. The convergence analysis relies on the framework of monotone operator splitting and the Krasnosel'skii-Mann fixed-point iteration with errors.

14 pages, 4 figures

References in corpus (4)

• A distributed primal-dual algorithm for computation of generalized Nash equilibria with shared affine coupling constraints via operator splitting methods
• Distributed GNE seeking under partial-decision information over networks via a doubly-augmented operator splitting approach
• Nash and Wardrop equilibria in aggregative games with coupling constraints
• Game Theoretic Methods for the Smart Grid

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• Nash equilibrium seeking under partial-decision information over directed communication networks
• Networked Aggregative Games with Linear Convergence
• Distributed Computation of Stochastic GNE with Partial Information: An Augmented Best-Response Approach