论文标题
零阶随机弱凸复合优化
Zero Order Stochastic Weakly Convex Composite Optimization
论文作者
论文摘要
在本文中，我们考虑随机弱凸复合问题，但是没有随机亚级别的甲骨文。我们提出了一种衍生的自由算法，该算法使用两个点近似来计算平滑函数的梯度估计。我们证明收敛的速度与最先进的方法相似，但是具有更大的常数，并报告了一些数值结果，显示了该方法的有效性。
In this paper we consider stochastic weakly convex composite 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.
