Distributed Optimization for Smart Cyber-Physical Networks

Abstract

The presence of embedded electronics and communication capabilities as well as sensing and control in smart devices has given rise to the novel concept of cyber-physical networks, in which agents aim at cooperatively solving complex tasks by local computation and communication. Numerous estimation, learning, decision and control tasks in smart networks involve the solution of large-scale, structured optimization problems in which network agents have only a partial knowledge of the whole problem. Distributed optimization aims at designing local computation and communication rules for the network processors allowing them to cooperatively solve the global optimization problem without relying on any central unit. The purpose of this survey is to provide an introduction to distributed optimization methodologies. Principal approaches, namely (primal) consensus-based, duality-based and constraint exchange methods, are formalized. An analysis of the basic schemes is supplied, and state-of-the-art extensions are reviewed.

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• ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks
• Robust Online Learning over Networks
• Distributed Safe Control Design and Probabilistic Safety Verification for Multi-Agent Systems
• Online Distributed Learning with Quantized Finite-Time Coordination
• Distributed Optimization Over Markovian Switching Random Network
• A Distributed Methodology for Approximate Uniform Global Minimum Sharing
• Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective
• Asynchronous Distributed Learning with Quantized Finite-Time Coordination
• Constraint-coupled Optimization with Unknown Costs: A Distributed Primal Decomposition Approach