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

In: Distributed Optimization: Advances in Theories, Methods, and Applications

Author

Listed:
  • 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)

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.

Suggested Citation

  • Huaqing Li & Qingguo Lü & Zheng Wang & Xiaofeng Liao & Tingwen Huang, 2020. "Achieving Linear Convergence of Distributed Optimization over Unbalanced Directed Networks with Row-Stochastic Weight Matrices," Springer Books, in: Distributed Optimization: Advances in Theories, Methods, and Applications, chapter 0, pages 7-31, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-6109-2_2
    DOI: 10.1007/978-981-15-6109-2_2
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