Distributed Optimization and Learning A Control-Theoretic Perspective |
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Author:
| Li, Zhongguo Ding, Zhengtao |
ISBN: | 978-0-443-21637-4 |
Publication Date: | Dec 2024 |
Publisher: | Elsevier Science & Technology Books
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Imprint: | Academic Press |
Book Format: | Ebook |
List Price: | USD $150.00USD $180.00 |
Book Description:
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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,...
More Description
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