An Event-Triggered Distributed Generalized Nash Equilibrium Seeking Algorithm

We consider the generalized Nash equilibrium (GNE) problem via distributed computation. Specifically, we consider a partial-decision information setting where each agent has no direct access to the decisions of all others while its cost function depends on them. Instead, each agent is assumed to exchange information with its neighbors via a communication network. To enhance communication efficiency, we propose a distributed event-triggered scheme such that each agent can independently determine when to transmit information to its neighbors. Thus, a fully-distributed, event-triggered GNE seeking algorithm is designed by combining event-triggered scheme, consensus, and projected-pseudo-gradient dynamics. Through primal-dual analysis, we prove convergence to a variational GNE by recasting the overall scheme as an inexact forward-backward iteration.