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We present and analyze a computational hybrid architecture for performingmulti-agent optimization. The optimization problems under consideration haveconvex objective and constraint functions with mild smoothness conditionsimposed on them. For such problems, we provide a primal-dual algorithmimplemented in the hybrid architecture, which consists of a decentralizednetwork of agents into which centralized information is occasionally injected,and we establish its convergence properties. To accomplish this, a centralcloud computer aggregates global information, carries out computations of thedual variables based on this information, and then distributes the updated dualvariables to the agents. The agents update their (primal) state variables andalso communicate among themselves with each agent sharing and receiving stateinformation with some number of its neighbors. Throughout, communications withthe cloud are not assumed to be synchronous or instantaneous, and communicationdelays are explicitly accounted for in the modeling and analysis of the system.Experimental results are presented to support the theoretical developmentsmade.

Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays