Distributed Optimization and Learning: A Control-Theoretic Perspective

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 allocationPresents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniquesOffers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches INDICE: Part I. Fundamental Concepts and Algorithms 1. Introduction to distributed optimisation and learning 2. A control perspective to single agent optimisation 3. Centralised optimisation and learning 4. Distributed frameworks. consensus, optimisation and learning 5. Distributed unconstrained optimisation 6. Constrained optimisation for resource allocation 7. Non-cooperative optimisation Part II. Advanced Algorithms and Applications 8. Output regulation to time-varying optimisation 9. Adaptive control to optimisation over directed graphs 10. Event-triggered control to optimal coordination 11. Fixed-time control to cooperative and competitive optimisation 12. Robust and adaptive control to competitive optimisation 13. Surrogate-model assisted algorithms to distributed optimisation 14. Discrete-time algorithms for supervised learning 15. Discrete-time output regulation for optimal robot coordination

• ISBN: 978-0-443-21636-7
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 350
• Fecha Publicación: 02/12/2024
• Nº Volúmenes: 1
• Idioma: Inglés