報告時間：2023年10月13日（星期五）15:30-17:00
報告地點：管理學院新大樓第二學術報告廳
報 告 人：劉俊驛 博士
工作單位：清華大學
舉辦單位：管理學院
報告簡介：
In this talk, we focus on a class of stochastic optimization problems that minimize the conditionally expected cost given a new covariate feature. In real-world OR applications, without the prior knowledge of the conditional probability distribution, it is hard to obtain scenarios under the covariate feature of interest. To deal with this challenge, we propose a data-driven piecewise affine decision rule (PADR) method based on historical data pairs. We provide the first non-asymptotic consistency of the data-driven PADR-based method for a broad class of decision-making problems under a minimal Lipschitz continuity assumption of the optimal decision rule. To solve the PADR-based empirical risk minimization problem with a coupled nonconvex and nondifferentiable structure, we develop an enhanced stochastic majorization minimization algorithm and provide the first non-asymptotic convergence rate in terms of directional stationarity. Numerical results for both convex and nonconvex stochastic optimization problems with various nonlinear generating models indicates the superiority of the proposed data-driven method compared with the state-of-the-art data-driven methods.
報告人簡介：
劉俊驛，清華大學工業工程系準聘副教授。2019年于美國南加州大學獲得工業于系統工程博士學位，2015年于中國科學技術大學少年班學院獲得統計專業學士學位。2019年9月至2021年3月在Prof. Jong-Shi Pang指導下從事博士后研究工作。目前研究方向為隨機優化，側重隨機優化與統計、機器學習的交叉研究。以第一作者身份在Operations Research, Mathematics of Operations Research, SIAM Journal on Optimization 等國際學術期刊上發表多篇文章。
