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
Table of Contents
Part I. Fundamental Concepts and Algorithms1. Introduction to distributed optimisation and learning2. A control perspective to single agent optimisation3. Centralised optimisation and learning4. Distributed frameworks. consensus, optimisation and learning5. Distributed unconstrained optimisation6. Constrained optimisation for resource allocation7. Non-cooperative optimisation Part II. Advanced Algorithms and Applications8. Output regulation to time-varying optimisation9. Adaptive control to optimisation over directed graphs10. Event-triggered control to optimal coordination11. Fixed-time control to cooperative and competitive optimisation12. Robust and adaptive control to competitive optimisation13. Surrogate-model assisted algorithms to distributed optimisation14. Discrete-time algorithms for supervised learning15. Discrete-time output regulation for optimal robot coordination
