Distributed Optimization and Learning
Language: English
Published by Elsevier Inc Jul 2024, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 388.61
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Add to basketItem description from seller
Neuware - 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.
Seller Inventory # 9780443216367
- Title
- Distributed Optimization and Learning
- Author
- Zhonggu Li
- Publisher
- Elsevier Inc Jul 2024
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443216363
- ISBN 13
- 9780443216367
- Item weight
- 387 grams
- Dimensions
- 229x152x15 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
Zhengtao Ding is a professor of control systems in the Department of Electrical and Electronic Engineering, at the University of Manchester, in the United Kingdom. He has authored and co-authored three books, including the book Nonlinear and Adaptive Control Systems (IET, 2013), and the book Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective (Academic Press, Elsevier, 2020). Dr. Ding has published over 300 research articles, with most of them in leading academic journals in his research area. His research interests include nonlinear and adaptive control theory and their applications, more recently network-based control, distributed optimization, and distributed machine learning, with applications to power systems and robotics. He serves as the Editor-in-Chief for Drones and Autonomous Vehicles, and the Specialty Chief Editor for Nonlinear Control for Frontiers in Control Engineering. He also serves as the Editor-in-Chief for an encyclopedia in the systems and control subject area, commissioned by Elsevier. He is an associate editor and past associate editor for a number of journals, including Scientific Reports, International Journal of Systems Sciences, Journal of Franklin Institute, IEEE Transactions on Automatic Control, among others. He is a member of the IEEE Technical Committee on Nonlinear Systems and Control, IEEE Technical Committee on Intelligent Control, and IFAC Technical Committee on Adaptive and Learning Systems. He is a Fellow of The Alan Turing Institute, the U.K.’s national institute for data science and artificial intelligence.
"About the title" may belong to another edition of this title.
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