报告题目：Optimization Methods for Acceleration: Adaptation, Subsampling and Subproblem-Solving
报 告 人：江波 教授 上海财经大学
报告时间：2021年1月4日 上午 9:00-9:40
报告地点：腾讯会议 ID：320 247 940
会议密码：9999
校内联系人：李欣欣 xinxinli@jlu.edu.cn
报告摘要：In this talk we present a suite of accelerated algorithms for solving optimization models arising from applications such as machine learning. In general, popular optimization methods for solving such problems include the high-order tensor approximation approach, which requires the knowledge on some problem parameters. To make such methods practical, one will need to find ways of implementation without such knowledge. Furthermore, when the objective in such models involves a large number of terms, a subsampling strategy will be adopted. We discuss methods that exhibit an accelerated iteration bound while maintaining the traits of being adaptive and allowing subsampling.
报告人简介：江波，上海财经大学信息管理与工程学院教授，于2013年9月在美国明尼苏达大学工业与系统工程系获得博士学位，导师张树中教授。主要研究领域包括优化理论，组合投资优化，信号处理，图像处理等。在运筹优化的国际一流杂志Operations Research, Mathematics of Operations Research, Mathematical Programming, SIAM Journal on Optimizatoin等发表过多篇论文。现为美国数学会旗下 mathmatical reviews 的评论员， 担任过Management Science, Mathematics of Operations Research, SIAM Journal on Optimization等著名期刊的匿名审稿人。曾在美国对冲基金公司Whitebox Advisors（该基金旗下管理资产约45亿美元）担任暑期研究员，从事鲁棒组合投资的研究工作。近期的主要研究课题为数据驱动的优化的理论及其在收益管理、运营管理中的应用。
