Distributed Coupled Multiagent Stochastic Optimization

• Published in: IEEE transactions on automatic control Vol. 65; no. 1; pp. 175 - 190
• Main Authors: Alghunaim, Sulaiman A., Sayed, Ali H.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Cost function; Coupled optimization; Coupling; Data models; Diffusion rate; diffusion strategy; Disease control; distributed optimization; Electric power distribution; Formulations; Learning; multiagent networks; Multiagent systems; Optimization; Parameters; penalty method; Predictive control; Stability analysis; stochastic optimization; Stochastic processes; Strategy; Wireless communication
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2019.2906495

This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a global parameter vector or model, and is subject to convex constraints that are only known locally. Problems of this type arise in several applications, most notably in disease propagation models, minimum-cost flow problems, distributed control formulations, and distributed power system monitoring. This paper focuses on stochastic settings, where a stochastic risk function is associated with each agent and the objective is to seek the minimizer of the aggregate sum of all risks subject to a set of constraints. Agents are not aware of the statistical distribution of the data and, therefore, can only rely on stochastic approximations in their learning strategies. We derive an effective distributed learning strategy that is able to track drifts in the underlying parameter model. A detailed performance and stability analysis is carried out showing that the resulting coupled diffusion strategy converges at a linear rate to an O(μ) neighborhood of the true penalized optimizer.