Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
报告专家：王承竞（西南交通大学）
报告时间：2024年6月20日（星期四）上午15：30-16：30
报告地点：研究生院三区204
报告摘要：Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computationally difficult to fit, particularly when the dimension of the data is large. To efficiently estimate the hub graphical models, we introduce a two-phase algorithm. The proposed algorithm first generates a good initial point via a dual alternating direction method of multipliers (ADMM), and then warm starts a semismooth Newton (SSN) based augmented Lagrangian method (ALM) to compute a solution that is accurate enough for practical tasks. We fully excavate the sparsity structure of the generalized Jacobian arising from the hubs in the graphical models, which ensures that the algorithm can obtain a nice solution very efficiently. Comprehensive experiments on both synthetic data and real data show that it obviously outperforms the existing state-of-the-art algorithms. In particular, in some high dimensional tasks, it can save more than 70\% of the execution time, meanwhile still achieves a high-quality estimation.
专家简介：王承竞，西南交通大学数学学院副教授，于浙江大学数学系取得计算数学博士学位，曾在新加坡国立大学参与博士后研究工作。他长期致力于大规模数值优化的理论、算法和软件研究，如协方差选取大规模半定规划问题、机器学习中的平方根lasso问题、支持向量机问题、遥感图像问题等的算法设计和软件实现。他在《SIAM Journal on Optimization》、《Journal of Machine Learning Research》、《IEEE Transactions on Signal Processing》等杂志发表了多篇文章。王承竞于2018年入选四川省学术和技术带头人后备人才，现为中国数学会计算数学分会理事。
邀请人：宋恩彬
