Bandit convex optimisation is a fundamental framework for studying zeroth-order convex optimisation. These notes cover 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.

翻译：赌博机凸优化是研究零阶凸优化的基本框架。本文涵盖了解决该问题所使用的多种工具，包括切割平面法、内点法、连续指数权重法、梯度下降法和在线牛顿步法。详细解释了众多假设与设置之间的细微差别。尽管文中并未包含大量真正新颖的内容，但一些现有工具被以创新的方式应用，从而产生了新算法。若干边界条件也得到了微小的改进。