报告人：张超(北京交通大学)

时间：2021年11月18日14:30开始

腾讯会议ID：129 674 841（无密码）

摘要：This paper considers a class of constrained convex stochastic composite optimization problems whose objective function is given by the summation of a differentiable convex component, together with a nonsmooth but convex component. The nonsmooth component has an explicit max structure that may not easy to compute its proximal mapping. In order to solve these problems, we propose a mini-batch stochastic Nesterov's smoothing (MSNS) method. Convergence and the optimal iteration complexity of the method are established. Numerical results are provided to illustrate the efficiency of the proposed MSNS method for a support vector machine (SVM) model.

简介:张超，女，北京交通大学理学院数学系，教授、博士生导师。博士毕业于日本弘前大学，目前担任北京交通大学理学院数学系教授。研究兴趣包括：最优化理论、方法及应用、运筹统计分析、最优化理论、算法及其应用等。已在 SIAM Journal on Scientific Computing、SIAM Journal on Optimization、Mathematical Programming、IEEE Transactions on Image Processing、Transportation Research 等一系列国际权威期刊上发表多篇论文。

邀请人：蒋 杰

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