题   目：Nonconvex truncated conditional value at risk-based sparse linear regression

报告人：谢博易 博士

单   位：南京大学

时   间：2026年2月7日 9:00

地   点：龙子湖校区九章学堂南楼C座302

摘   要：Conditional value at risk (CVaR) is a widely recognized risk measure used to manage data uncertainty within risk management. In this paper, we study a class of sparse linear regression models based on truncated CVaR measure and \ell_0-norm regularization. Due to the nonconvexity and nonsmoothness of the objective functions, as well as the NP-hardness of the problem with the \ell_0-norm regularization, we propose an approximation model that employs a tight relaxation of the \ell_0-norm. The solution equivalence between the proposed model and its approximation model is explored. To efficiently solve the approximation model, we develop a semismooth Newton-based proximal majorization-minimization algorithm. Furthermore, the convergence analysis of the proposed algorithm is presented, and the convergence rate for the reduced CVaR-based sparse linear regression model is established. Moreover, extensive numerical experiments conducted on both synthetic and real datasets validate the stability and effectiveness of the proposed algorithm, demonstrating significant improvements in both sparsity and accuracy compared to existing state-of-the-art methods.

报告人简介：南京大学工程管理学院2023级博士生。研究方向为：最优化理论、方法及在管理科学中的应用；多属性决策分析。已在European Journal of Operational Research等国际期刊发表学术论文3篇；主持2025年江苏省博士生科研创新计划项目1项；荣获第十一届中国运筹学会数学规划分会研究生论坛“优秀报告奖”。