eBook - ePub
Distributed Optimization and Learning
A Control-Theoretic Perspective
- 350 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
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
Information
Table of contents
- Distributed Optimization and Learning
- Cover
- Title page
- Copyright
- Table of Contents
- Dedication
- Biography
- Preface
- 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
- 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
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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 & Artificial Intelligence (AI) & Semantics. We have over 1.5 million books available in our catalogue for you to explore.