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Distributed Optimization and Learning
A Control-Theoretic Perspective
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- 350 pages
- English
- ePUB (mobile friendly)
- Only available on web
eBook - ePub
Available until 21 Sep |Learn more
Book details
Table of contents
Citations
About This Book
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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Yes, you can access Distributed Optimization and Learning by Zhongguo Li,Zhengtao Ding in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Mechanical Engineering. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Biography
- Preface
- Part I: Fundamental concepts and algorithms
- Chapter 1: Introduction to distributed optimization and learning
- Chapter 2: Control perspective to single agent optimization
- Chapter 3: Distributed frameworks: Graphs, consensus, optimization, and learning
- Chapter 4: Distributed unconstrained optimization
- Chapter 5: Distributed constrained optimization for resource allocation
- Chapter 6: Non-cooperative optimization
- Part II: Advanced algorithms and applications
- Chapter 7: Output regulation to time-varying optimization
- Chapter 8: Adaptive approach to optimization
- Chapter 9: Fixed-time control to optimization
- Chapter 10: Surrogate-assisted algorithms to distributed optimization
- Chapter 11: Discrete-time algorithms to supervised learning
- Chapter 12: Game theory-based distributed algorithm for electricity market
- Bibliography
- Index