Asynchronous Broadcast-Based Distributed Optimization over Unbalanced Directed Networks

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 4 in Distributed Optimization: Advances in Theories, Methods, and Applications, 2020, pp 57-84 from Springer

Abstract: Abstract In recent years, due to extensive applications of robotics, sensors, power and wireless communication networks in the field of machine learning, signal processing and control, decentralized optimization has been widely studied [1–5]. Quite a few problems occurring in such fields can be transformed into distributed convex optimization problems on multi-agent networks, in which each single agent works together to minimize a shared cost function in the circumstance of various constraints. A salient feature of solving distributed convex optimization problems is that an agent only communicates with its in-neighbors. The communication architecture can be formulated as a network, often directed and/or unbalanced. These problems typically demand the design of optimization algorithms being completely distributed, that is, algorithms are implemented by agents without a central coordinator [6]. Moreover, these observations naturally need algorithms to solve problems that can work well merely based on local information exchange and are robust against changes of network topologies and potential link failures.

Date: 2020
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DOI: 10.1007/978-981-15-6109-2_4

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