A zeroth order method for stochastic weakly convex optimization
本文针对没有随机次梯度信息的弱凸优化问题，提出一种基于两点梯度近似的无导数算法，其收敛速度与现有方法相当但常数更大，数值实验验证了有效性。
Abstract In this paper, we consider stochastic weakly convex optimization problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that uses a two point approximation for computing a gradient estimate of the smoothed function. We prove convergence at a similar rate as state of the art methods, however with a larger constant, and report some numerical results showing the effectiveness of the approach.