题    目：Double-proximal augmented Lagrangian methods with improved convergence condition

主讲人：白建超 副教授

单    位：西北工业大学

时    间：2025年12月25日 15:30

地    点：九章学堂南楼C座302

摘    要：In this talk, a double-proximal augmented Lagrangian method (DP-ALM) will be presented for a family of linearly constrained convex minimization problems whose objective function is not necessarily smooth. By a new prediction-correction reformulation for this DP-ALM and similar variational characterizations for both the saddle-point of the problem and the generated sequences, we establish its global convergence and sublinear convergence rate in both ergodic and nonergodic senses. A toy example is taken to illustrate that the presented lower bound of proximal parameter is optimal (smallest). We also discuss a relaxed accelerated version as well as a linearized version of DP-ALM when the objective function has composite structures. Experiments results on solving two large-scale sparse optimization problems show that our proposed methods outperform some well-established methods.

简    介：白建超，博士(后)，西北工业大学长聘副教授、博士生导师。主要从事数据科学与智能科学等领域的优化模型、算法设计与分析，在Automatica. Computational Optimization and Applications、European Journal of Operational Research、SIAM Journal on Imaging Sciences、IEEE Transactions on Medical Imaging、Journal of Scientific Computing等期刊上发表论文40余篇，其中ESI高被引论文3篇。他曾主持国家自然科学基金面上项目、青年项目和多项省部级项目，现担任国家自然科学基金评议专家、北京市/广东省/福建省等自然科学基金评议专家、CSIAM大数据与人工智能专委会委员以及SCI期刊Scientific Reports编委等。