Bandit Convex Optimisation

AvTor Lattimore

Inbunden, Engelska, 2026

627 kr

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E-bok

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Beskrivning

This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimization and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.

Produktinformation

Utgivningsdatum:2026-03-19
Mått:157 x 235 x 20 mm
Vikt:567 g
Format:Inbunden
Språk:Engelska
Antal sidor:280
Förlag:Cambridge University Press
ISBN:9781009607599

Utforska kategorier

Optimering inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT

Mer om författaren

Tor Lattimore is a researcher at Google DeepMind working on reinforcement learning, bandits, optimisation and the theory of machine learning. He is the co-author of an introductory book on bandit algorithms and has published nearly 100 conference and journal articles. He is an action editor for the Journal of Machine Learning Research.

Recensioner i media

'A landmark text on bandit convex optimization by an authority in the field. This book develops the full theory of zeroth-order online convex optimization-where one must learn from noisy function values without gradients-establishing regret bounds and presenting elegant algorithms from gradient descent to cutting planes, multiplicative updates, and Newton methods. Touching on all areas central to advanced optimization, it is an essential companion for researchers, offering both the conceptual foundations and the algorithmic toolkit that continue to drive progress in online convex optimization and mathematical optimization more broadly.' Elad Hazan, Princeton University

Innehållsförteckning

Preface; 1. Introduction and problem statement; 2. Overview of methods and history; 3. Mathematical tools; 4. Bisection in one dimension; 5. Online gradient descent; 6. Self-concordant regularisation; 7. Linear and quadratic bandits; 8. Exponential weights; 9. Cutting plane methods; 10. Online Newton step; 11. Online Newton step for adversarial losses; 12. Gaussian optimistic smoothing; 13. Submodular minimisation; 14. Outlook; Appendix A. Miscellaneous; Appendix B. Concentration; Appendix C. Notation; Bibliography; Index.