Distributed Optimization and Learning
A Control-Theoretic Perspective
- 1st Edition - July 18, 2024
- Imprint: Academic Press
- Authors: Zhongguo Li, Zhengtao Ding
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 1 6 3 6 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 1 6 3 7 - 4
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systemati… Read more
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Request a sales quoteDistributed 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
Researchers, industrial practitioners, graduate students in optimization, control engineering, data sciences, machine learning, mechatronics, and applied mathematics, Mathematicians and engineers working on optimisation, learning and control systems, 3rd/4th-year undergraduate students with interests in multi-agent system optimization and control, robotics and machine learning
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Biography
- Zhongguo Li
- Zhengtao Ding
- Preface
- Part I: Fundamental concepts and algorithms
- Chapter 1: Introduction to distributed optimization and learning
- Abstract
- 1.1. Background
- 1.2. Literature review
- 1.3. Book organization
- Bibliography
- Chapter 2: Control perspective to single agent optimization
- Abstract
- 2.1. Unconstrained optimization
- 2.2. Set constrained optimization
- 2.3. Optimization with affine constraints
- 2.4. Discrete-time control perspective to optimization
- Bibliography
- Chapter 3: Distributed frameworks: Graphs, consensus, optimization, and learning
- Abstract
- 3.1. Centralized networks: Structures and limitations
- 3.2. Centralized optimization and learning
- 3.3. Graph theory
- 3.4. Fundamental consensus algorithms
- 3.5. Preliminary lemmas
- Bibliography
- Chapter 4: Distributed unconstrained optimization
- Abstract
- 4.1. Introduction to distributed unconstrained optimization
- 4.2. Problem formulation
- 4.3. Algorithm design and convergence analysis
- Bibliography
- Chapter 5: Distributed constrained optimization for resource allocation
- Abstract
- 5.1. Introduction
- 5.2. Problem formulation
- 5.3. Algorithm design and convergence analysis
- Bibliography
- Chapter 6: Non-cooperative optimization
- Abstract
- 6.1. Nash equilibrium problem
- 6.2. Game formulation on networks
- 6.3. Distributed gossip-based algorithm design
- 6.4. Convergence analysis
- 6.5. Simulation results
- Bibliography
- Part II: Advanced algorithms and applications
- Chapter 7: Output regulation to time-varying optimization
- Abstract
- 7.1. Introduction
- 7.2. Problem formulation
- 7.3. Preliminaries
- 7.4. Distributed Optimization Algorithm I
- 7.5. Distributed Optimization Algorithm II
- 7.6. Simulation studies
- 7.7. Summary
- Bibliography
- Chapter 8: Adaptive approach to optimization
- Abstract
- 8.1. Adaptive preference formulation for cooperative multi-objective resource allocation
- 8.2. Adaptive again design for non-cooperative Nash equilibrium seeking
- Bibliography
- Chapter 9: Fixed-time control to optimization
- Abstract
- 9.1. Time-varying multi-objective optimization
- 9.2. Fixed-time Nash equilibrium searching
- Bibliography
- Chapter 10: Surrogate-assisted algorithms to distributed optimization
- Abstract
- 10.1. Introduction
- 10.2. Problem formulation
- 10.3. Main results
- 10.4. Simulation study
- 10.5. Summary
- Bibliography
- Chapter 11: Discrete-time algorithms to supervised learning
- Abstract
- 11.1. Introduction
- 11.2. Problem formulation and algorithm development
- 11.3. Performance analysis
- 11.4. Simulation and discussion
- 11.5. Summary
- Bibliography
- Chapter 12: Game theory-based distributed algorithm for electricity market
- Abstract
- 12.1. Introduction
- 12.2. Problem formulation
- 12.3. Distributed Nash equilibrium seeking algorithm
- 12.4. Case studies
- 12.5. Summary
- Bibliography
- Bibliography
- Bibliography
- Index
- Edition: 1
- Published: July 18, 2024
- Imprint: Academic Press
- No. of pages: 350
- Language: English
- Paperback ISBN: 9780443216367
- eBook ISBN: 9780443216374
ZL
Zhongguo Li
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
Affiliations and expertise
Department of Computer Science, University College London, UKZD
Zhengtao Ding
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.
Affiliations and expertise
Department of Electrical and Electronic Engineering, University of Manchester, UKRead Distributed Optimization and Learning on ScienceDirect