Achieving Linear Convergence of Distributed Optimization over Unbalanced Directed Networks with Row-Stochastic Weight Matrices

Huaqing Li (), Qingguo Lü, Zheng Wang, Xiaofeng Liao and Tingwen Huang
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Huaqing Li: Southwest University, College of Electronic and Information Engineering
Qingguo Lü: Southwest University, College of Electronic and Information Engineering
Zheng Wang: Southwest University, College of Electronic and Information Engineering
Xiaofeng Liao: Chongqing University, College of Computer Science
Tingwen Huang: Texas A&M University at Qatar, Science Program

Chapter Chapter 2 in Distributed Optimization: Advances in Theories, Methods, and Applications, 2020, pp 7-31 from Springer

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1007/978-981-15-6109-2_2

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