2017/09/25 by Angelia Nedić, Alex Olshevsky, Nedić, Angelia +3 · 12 citations
Computer Science · #Cooperative Communication and Network Coding #Distributed #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Mathematics #Multiagent Systems (cs.MA) #Optimization and Control (math.OC) #Parallel #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1709.08765
openalex publication_date 2017/09/25 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
In decentralized optimization, nodes cooperate to minimize an overall\nobjective function that is the sum (or average) of per-node private objective\nfunctions. Algorithms interleave local computations with communication among\nall or a subset of the nodes. Motivated by a variety of\napplications---distributed estimation in sensor networks, fitting models to\nmassive data sets, and distributed control of multi-robot systems, to name a\nfew---significant advances have been made towards the development of robust,\npractical algorithms with theoretical performance guarantees. This paper\npresents an overview of recent work in this area. In general, rates of\nconvergence depend not only on the number of nodes involved and the desired\nlevel of accuracy, but also on the structure and nature of the network over\nwhich nodes communicate (e.g., whether links are directed or undirected, static\nor time-varying). We survey the state-of-the-art algorithms and their analyses\ntailored to these different scenarios, highlighting the role of the network\ntopology.\n