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。本讲义涵盖了解决该问题所使用的多种工具，包括割平面法、内点法、连续指数加权法、梯度下降法和在线牛顿步法。详细阐述了多种假设与设置之间的细微差别。尽管本文并未真正提出大量新内容，但通过创新性地应用部分现有工具，我们获得了新的算法。此外，若干边界条件也得到了小幅改进。