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.
1. Chapter 1 Introduction to distributed optimization and learning - p. 3 2. Chapter 2 Control perspective to single agent optimization - p. 13 3. Chapter 3 Distributed frameworks: Graphs, consensus, optimization, and learning - p. 27 4. Chapter 4 Distributed unconstrained optimization - p. 39 5. Chapter 5 Distributed constrained optimization for resource allocation - p. 47 6. Chapter 6 Non-cooperative optimization - p. 57 7. Chapter 7 Output regulation to time-varying optimization - p. 73 8. Chapter 8 Adaptive approach to optimization - p. 99 9. 8.1: Adaptive preference formulation for cooperative multi-objective resource allocation - p. 99 10. 8.2: Adaptive again design for non-cooperative Nash equilibrium seeking - p. 121 11. 8.2.1: Introduction - p. 121 12. 8.2.2: Problem formulation - p. 123 13. 8.2.3: Algorithms design and convergence analysis - p. 126 14. 8.2.4: Distributed power control of femtocell networks - p. 138 15. 8.2.5: Summary - p. 147 16. Chapter 9 Fixed-time control to optimization - p. 149 17. 9.1: Time-varying multi-objective optimization - p. 149 18. 9.2: Fixed-time Nash equilibrium searching - p. 168 19. Chapter 10 Surrogate-assisted algorithms to distributed optimization - p. 183 20. Chapter 11 Discrete-time algorithms to supervised learning - p. 207 21. Chapter 12 Game theory-based distributed algorithm for electricity market - p. 231 22. Bibliography - p. 251 23. Index - p. 263
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.
1. Chapter 1 Introduction to distributed optimization and learning - p. 3 2. Chapter 2 Control perspective to single agent optimization - p. 13 3. Chapter 3 Distributed frameworks: Graphs, consensus, optimization, and learning - p. 27 4. Chapter 4 Distributed unconstrained optimization - p. 39 5. Chapter 5 Distributed constrained optimization for resource allocation - p. 47 6. Chapter 6 Non-cooperative optimization - p. 57 7. Chapter 7 Output regulation to time-varying optimization - p. 73 8. Chapter 8 Adaptive approach to optimization - p. 99 9. 8.1: Adaptive preference formulation for cooperative multi-objective resource allocation - p. 99 10. 8.2: Adaptive again design for non-cooperative Nash equilibrium seeking - p. 121 11. 8.2.1: Introduction - p. 121 12. 8.2.2: Problem formulation - p. 123 13. 8.2.3: Algorithms design and convergence analysis - p. 126 14. 8.2.4: Distributed power control of femtocell networks - p. 138 15. 8.2.5: Summary - p. 147 16. Chapter 9 Fixed-time control to optimization - p. 149 17. 9.1: Time-varying multi-objective optimization - p. 149 18. 9.2: Fixed-time Nash equilibrium searching - p. 168 19. Chapter 10 Surrogate-assisted algorithms to distributed optimization - p. 183 20. Chapter 11 Discrete-time algorithms to supervised learning - p. 207 21. Chapter 12 Game theory-based distributed algorithm for electricity market - p. 231 22. Bibliography - p. 251 23. Index - p. 263
