报告题目：Feature splitting parallel algorithm for Dantzig Selectors
时 间：2026年4月11日（星期六）10：30
地 点：科研楼18号楼1102
主 办：数学与统计学院，分析数学及应用教育部重点实验室，福建省分析数学及应用重点实验室
参加对象：相关专业师生
报告摘要：The Dantzig selector is a widely used and effective method for variable selection in ultra-high-dimensional data. Feature splitting is an efficient processing technique that involves dividing these ultra-high-dimensional variable datasets into manageable subsets that can be stored and processed more easily on a single machine. This paper proposes a variable splitting parallel algorithm for solving both convex and nonconvex Dantzig selectors based on the proximal point algorithm. The primary advantage of our parallel algorithm, compared to existing parallel approaches, is the significantly reduced number of iteration variables, which greatly enhances computational efficiency and accelerates the convergence speed of the algorithm. Furthermore, we show that our solution remains unchanged regardless of how the data is partitioned, a property referred to as partition insensitive. In theory, we use a concise proof framework to demonstrate that the algorithm exhibits linear convergence. Numerical experiments indicate that our algorithm performs competitively in both parallel and nonparallel environments.
报告人简介：张志民，重庆大学教授，博士生导师，重庆市学术技术带头人，中国工业与应用数学学会第八届、九届理事会理事，中国优选法统筹法与经济数学研究会量化金融与保险分会常务理事，中国工业与应用数学学会金融数学、金融工程与精算保险专委会委员。研究兴趣为金融统计与保险精算，发表高水平SCI论文90余篇，主持国家自然科学基金4项，省部级与横向课题10余项。