论文标题
凸优化中的随机无梯度方法
Randomized gradient-free methods in convex optimization
论文作者
论文摘要
这篇评论提出了解决凸优化问题的现代无梯度方法。通过无梯度方法，我们的意思是那些仅使用（嘈杂）实现目标值的方法。我们受到各种应用的激励，这些应用程序梯度信息过高甚至无法使用。我们主要关注三个标准：甲骨文的复杂性，迭代复杂性和最大允许噪声水平。
This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective value. We are motivated by various applications where gradient information is prohibitively expensive or even unavailable. We mainly focus on three criteria: oracle complexity, iteration complexity, and the maximum permissible noise level.
