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• W3100355826 startingPage "1584" @default.
• W3100355826 abstract "We consider distributed computation of generalized Nash equilibrium (GNE) over networks, in games with shared coupling constraints. Existing methods require that each player has full access to opponents' decisions. In this paper, we assume that players have only partial-decision information, and can communicate with their neighbours over an arbitrary undirected graph. We recast the problem as that of finding a zero of a sum of monotone operators through primal-dual analysis. To distribute the problem, we doubly augment variables, so that each player has local decision estimates and local copies of Lagrangian multipliers. We introduce a single-layer algorithm, fully distributed with respect to both primal and dual variables. We show its convergence to a variational GNE with fixed step-sizes, by reformulating it as a forward-backward iteration for a pair of doubly-augmented monotone operators." @default.
• W3100355826 created "2020-11-23" @default.
• W3100355826 creator A5038039270 @default.
• W3100355826 date "2020-04-01" @default.
• W3100355826 modified "2023-10-17" @default.
• W3100355826 title "Distributed GNE Seeking Under Partial-Decision Information Over Networks via a Doubly-Augmented Operator Splitting Approach" @default.
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• W3100355826 doi "https://doi.org/10.1109/tac.2019.2922953" @default.
• W3100355826 hasPublicationYear "2020" @default.
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