This paper considers an event-driven distributed non-convex optimization algorithm for a multi-agent system, where each agent has a non-convex cost function. The goal of the multi-agent system is to minimize the globa...
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This paper considers an event-driven distributed non-convex optimization algorithm for a multi-agent system, where each agent has a non-convex cost function. The goal of the multi-agent system is to minimize the global objective function, which is the sum of these local cost functions, in a distributed manner. To this end, each agent updates the own state by a consensus-based gradientdescent algorithm. The local information exchange among neighbor agents is carried out with an event-triggered scheme to achieve consensus with less inter-agent communication. Convergence to a critical point of the objective function and the validity of the proposed algorithm in numerical examples are shown.
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