Distributed Optimization in Networked Systems (eBook)

This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.

Qingguo Lü received his PhD degree in Computational Intelligence and Information Processing from Southwest University, Chongqing, China, in 2021. He is currently a Hongshen young teacher (special support) at the College of Computer Science, Chongqing University, China. He was a Research Associate with Texas A&M University at Qatar from Jun. to Sept. 2019. He has published more than 30 research papers and 2 monographs on distributed optimization in networked systems. He is an IEEE/ACM Member.

Xiaofeng Liao received his PhD degree in Circuits and Systems from the University of Electronic Science and Technology of China, Chengdu, China, in 1997. He is currently a Professor and Dean of the College of Computer Science, Chongqing University, China. He is also a Yangtze River Scholar of the Ministry of Education of China, Beijing, China. From 1999 to 2012, he was a Professor with Chongqing University. From 2012 to 2018, he was a Professor and Dean of the College of Electronic and Information Engineering, Southwest University, Chongqing. From Nov. 1997 to Apr. 1998, he was a Research Associate with the Chinese University of Hong Kong. From Oct. 1999 to Oct. 2000, he was a Research Associate with the City University of Hong Kong. From Mar. 2001 to Jun. 2001 and Mar. 2002 to Jun. 2002, he was a Senior Research Associate at the City University of Hong Kong. From Mar. 2006 to Apr. 2007, he was a Research Fellow at the City University of Hong Kong. He has published more than 400 research papers and 5 monographs on computer science. Prof. Liao currently serves as an Editorial Board Member for IEEE Transactions on Neural Networks and Learning Systems, Chinese Journal of Electronics, Big Data Mining and Analytics, etc. He is an IEEE/AIAA Fellow.

Huaqing Li received his PhD degree in Computer Science from Chongqing University, China in 2013. He is currently a professor at the College of Electronic and Information Engineering, Southwest University, Chongqing, China. He was a Postdoctoral Research Associate with the University of Sydney, Australia from 2014 to 2015, and a Research Fellow with Nanyang Technological University, Singapore from 2015 to 2016. He has published more than 80 research papers and 3 monographs on distributed optimization in networked systems. Prof. Li currently serves as an Editorial Board Member for Neural Computing and Applications, Frontiers of Information Technology & Electronic Engineering, and IEEE Access. He is an IEEE Senior Member.

Shaojiang Deng received his PhD degree in Computer Science from Chongqing University, China in 2005. He is currently a professor at the College of Computer Science, Chongqing University, China. In 2007, he was a Visiting Scholar with the Institute of Applied Computer Science, Dresden University of Technology, Germany. He has published more than 80 research papers and 1 monograph on computer science.

Shanfu Gao received his BS degree in Ecommerce Management from China University of Mining and Technology, Xuzhou, Jiangsu, China, in 2021. He is currently pursuing his MS degree in Electronic Information at Chongqing University, China.

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This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.