报告题目:Distributionally robust second-order stochastic dominance
报告人:刘嘉副教授
邀请人:彭深
报告时间:2022年6月21日8:30-10:00
腾讯会议ID:759-632-407
报告人简介:刘嘉,西安交通大学williamhill威廉希尔官网科学计算系副教授。本科、硕士、博士均毕业于西安交通大学,期间赴法国巴黎第11大学联合培养。研究兴趣包括随机优化、鲁棒优化等近现代优化方法,强化学习等人工智能方法,及其在金融工程中的应用。他在这些方向取得了一些研究结果,在Mathematics of Operations Research、SIAM Journal on Optimization、European Journal of Operational Research、Quantitative Finance等运筹学、金融学期刊上发表学术论文30余篇,主持国家自然科学基金青年项目一项,参与国家自然科学基金重大、重点、面上项目以及与深圳证券交易所、华为软件有限公司、上海电气集团、中航工业集团西安航空计算技术研究所等单位合作的横向课题十余项。。
报告摘要:In this talk, we consider a distributionally robust second-order stochastic dominance constrained optimization problem. We require the dominance constraints hold with respect to all probability distributions in a Wasserstein ball centered at the empirical distribution. We adopt the sample approximation approach to develop a linear programming formulation that provides a lower bound. We propose a novel split-and-dual decomposition framework which provides an upper bound. We establish quantitative convergence for both lower and upper approximations given some constraint qualification conditions. To efficiently solve the non-convex upper bound problem, we use a sequential convex approximation algorithm. Numerical evidences on a portfolio selection problem valid the convergence and effectiveness of the proposed two approximation methods.
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