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Bandit Convex Optimisation

Optimization and Control 2025-11-13 v5 Machine Learning Machine Learning

Abstract

Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. This book covers the many tools used for this problem, including cutting plane methods, interior point methods, continuous exponential weights, gradient descent and online Newton step. The nuances between the many assumptions and setups are explained. Although there is not much truly new here, some existing tools are applied in novel ways to obtain new algorithms. A few bounds are improved in minor ways.

Keywords

Cite

@article{arxiv.2402.06535,
  title  = {Bandit Convex Optimisation},
  author = {Tor Lattimore},
  journal= {arXiv preprint arXiv:2402.06535},
  year   = {2025}
}

Comments

274 pages

R2 v1 2026-06-28T14:44:15.512Z