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Distributed Stochastic Optimization: Variance Reduction and Edge-Based Method

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

The abstraction of distributed optimization is to achieve optimal decision making or control by local manipulation with private data and diffusion of local information through a network of computational nodes. Due to the promising prospects in machine learning, statistical computation [1, 2], and extensive applications for power systems, sensor networks, and wireless communication networks [3, 4], distributed optimization has harvested many attentions over the years. Most of issues arisen in these fields are cast as distributed optimization problems, in which nodes of a network collaboratively optimize a global objective function through operating on their local objective functions and communicating with their neighbors only.

Suggested Citation

  • Huaqing Li & Qingguo Lü & Zheng Wang & Xiaofeng Liao & Tingwen Huang, 2020. "Distributed Stochastic Optimization: Variance Reduction and Edge-Based Method," Springer Books, in: Distributed Optimization: Advances in Theories, Methods, and Applications, chapter 0, pages 161-188, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-6109-2_8
    DOI: 10.1007/978-981-15-6109-2_8
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