报告题目：A stochastic linearized proximal method of multipliers for convex stochastic optimization with expectation constraints

报告人：张立卫 教授

报告时间：2022年5月26日9:00

报告地点：腾讯会议：604-759-077

邀请单位：bat365在线登录入口，福建省应用数学中心（bat365）

报告内容简介：

This talk considers the problem of minimizing a convex expectation function with a set of inequality convex expectation constraints. We present a computable stochastic approximation type algorithm, namely the stochastic linearized proximal method of multipliers, to solve this convex stochastic optimization problem. This algorithm can be roughly viewed as a hybrid of stochastic approximation and the traditional proximal method of multipliers. Under mild conditions, we show that this algorithm exhibits                                               expected convergence rates for both objective reduction and constraint violation if parameters in the algorithm are properly chosen, where K denotes the number of iterations. Moreover, we show that, with high probability, the algorithm has constraint violation bound and ) objective bound. Some preliminary numerical results demonstrate the performance of the proposed algorithm.

报告人简介：

张立卫教授，大连理工大学数学科学学院运筹学与控制论业博士生指导教师，金融数学与保险精算专业博士生指导教师。

他于1989年，1992年，1998年分别在大连理工大学获得理学学士，硕士，博士学位，1999-2001在中科院计算数学所从事博士后工作。目前的研究兴趣是“矩阵优化”，“随机规划”与“均衡优化”。他完成和主持自然科学基金面上基金多项,重点基金子课题两项。在国际顶级期刊Math. Programming, Operations Research, SIAM J. Optimization, Mathematics of Operations Research, Mathematics of Computation 发表论文10余篇，2020年获得中国运筹学会运筹研究奖，现任中国运筹学会常务理事，中国运筹学会数学规划分会副理事长，中国运筹学会金融工程与金融风险管理分会副理事长,《APJOR》和《运筹学学报》编委。