Zhongguo Li, Zhengtao Ding

Distributed Optimization and Learning (eBook, ePUB)

A Control-Theoretic Perspective

• Format: ePub

Produktbeschreibung

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. - Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation - Presents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniques - Offers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 350
• Erscheinungstermin: 18. Juli 2024
• Englisch
• ISBN-13: 9780443216374
• Artikelnr.: 72251781

Autorenporträt

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

Inhaltsangabe

Part I. Fundamental Concepts and Algorithms 1. Introduction to distributed
optimisation and learning 2. A control perspective to single agent
optimisation 3. Centralised optimisation and learning 4. Distributed
frameworks. consensus, optimisation and learning 5. Distributed
unconstrained optimisation 6. Constrained optimisation for resource
allocation 7. Non-cooperative optimisation Part II. Advanced Algorithms and
Applications 8. Output regulation to time-varying optimisation 9. Adaptive
control to optimisation over directed graphs 10. Event-triggered control to
optimal coordination 11. Fixed-time control to cooperative and competitive
optimisation 12. Robust and adaptive control to competitive optimisation
13. Surrogate-model assisted algorithms to distributed optimisation 14.
Discrete-time algorithms for supervised learning 15. Discrete-time output
regulation for optimal robot coordination