Code and Data Repository for Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm
该软件用于比较论文提出的随机线性化近端乘子法（SLPMM）与几种现有算法在最小化凸期望函数并满足不等式凸期望约束时的性能。
The goal of this software is to compare the performance of stochastic linearized proximal method of multipliers (SLPMM) proposed in the paper with several existing algorithms for minimizing a convex expectation function subject to a set of inequality convex expectation constraints.