Over the past several years, under the great progress of multi-agent networks in emerging areas, increasing number of investigators have conducted in-depth research and achieved remarkable results. With the universalization of networked control systems, multi-agent networks not only introduce a theoretical analysis approach for modeling and analyzing dynamic systems, but also have a crucial role to play in studying distributed artificial intelligence [1–6]. Distributed coordination and optimization of networked control systems, as a significant topic in the study of multi-agent networks, have gained considerable interest and great attention. Specifically, this class of problem has found a number of engineering applications, e.g., distributed state estimation [7], resource allocation [8], regression [9, 10], as well as machine learning [11–13], among many others.
Achieving Linear Convergence of Distributed Optimization over Unbalanced Directed Networks with Row-Stochastic Weight Matrices
Distributed Optimization: Advances in Theories, Methods, and Applications ; Chapter : 2 ; 7-31
2020-08-05
25 pages
Article/Chapter (Book)
Electronic Resource
English
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