A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming
提出一种随机化梯度方法，用于处理梯度计算困难的凸函数，适用于概率最大化和概率约束问题，并讨论了梯度估计的模拟过程。
Abstract We propose a randomized gradient method for handling a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach is adapted to probability maximization and probabilistic constrained problems. We discuss simulation procedures for gradient estimation.