Book contents
- Frontmatter
- Contents
- 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
- References
- Index
14 - Outlook
Published online by Cambridge University Press: 07 March 2026
Book contents
- Frontmatter
- Contents
- 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
- References
- Index
Summary
The tool-chest for convex bandits and zeroth-order optimisation has been steadily growing in recent decades. Nevertheless, there are many intriguing open questions, both theoretical and practical. The purpose of this short chapter is to highlight some of the most important (in the author’s view, of course) open problems.
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- Bandit Convex Optimisation , pp. 239 - 241Publisher: Cambridge University PressPrint publication year: 2026
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