Distributed Stochastic Optimization: Variance Reduction and Edge-Based Method

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 8 in Distributed Optimization: Advances in Theories, Methods, and Applications, 2020, pp 161-188 from Springer

Abstract: 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.

Date: 2020
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DOI: 10.1007/978-981-15-6109-2_8

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