Distributed Optimization and Learning

Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.

Zhongguo Li is a lecturer in robotics and AI at the Department of Computer Science, University College London, in the U.K. His research interests focus on developing advanced optimization and learning algorithms for cooperative and competitive multi-agent systems. His research has revealed fundamental but crucial relationships among control, optimization, and learning in complex networked systems. His research not only contributes significantly to theoretical guarantees of desired optimal behaviors, but also catalyzes a number of engineering applications in optimal and sustainable scheduling of power resources and wind farms. He is one of the most active researchers in distributed optimisation and learning. In his research field, he has authored or co-authored more than 20 papers in well-recognised journals and conferences, including IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Cybernetics, and IEEE Transactions on Neural Networks and Learning Systems, among others. Dr. Li serves as an Associate Editor for Drones and Autonomous Vehicles, and a Guest Editor for Frontiers in Control Engineering. He is an active reviewer for top journals such as IEEE Transactions on Neural Networks and Learning Systems, Automatica, and IEEE Transactions on Cybernetics