Zhongguo Li,
Zhengtao Ding
Distributed Optimization and Learning
A Control-Theoretic Perspective
Paperback Engels 2024 9780443216367Levertijd ± 9 werkdagen
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Samenvatting
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.
Specificaties
ISBN13:9780443216367
Taal:Engels
Bindwijze:Paperback
Aantal pagina's:286
Uitgever:Elsevier Science
Hoofdrubriek:Wetenschap en techniek, Computer en informatica
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Inhoudsopgave
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
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
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