报告题目：Partition-Insensitive Parallel ADMM Algorithm for High-dimensional Linear Models

报告人：蒋建成

报告时间：2024年12月17日 上午 8点30分—9点30分

报告地点：腾讯会议（756 430 668）

报告摘要：

Abstract. The parallel alternating direction method of multipliers (ADMM) algorithms have gained popularity in statistics and machine learning due to their efficient handling of large sample data problems. However, the parallel structure of these algorithms, based on the consensus problem, can lead to an excessive number of auxiliary variables when applied to highdimensional data, resulting in large computational burden. In this paper, we propose a partition-insensitive parallel framework based on the linearized ADMM (LADMM) algorithm and apply it to solve nonconvex penalized high-dimensional regression problems. Compared to existing parallel ADMM algorithms, our algorithm does not rely on the consensus problem,

resulting in a significant reduction in the number of variables that need to be updated at each iteration. It is worth noting that the solution of our algorithm remains largely unchanged regardless of how the total sample is divided, which is known as partition-insensitivity. Furthermore, under some mild assumptions, we prove the convergence of the iterative sequence generated by our parallel algorithm. Numerical experiments on synthetic and real datasets demonstrate the feasibility and validity of the proposed algorithm. We provide a publicly available R software package to facilitate the implementation of the proposed algorithm.

报告人简介：

蒋建成，北卡罗来纳大学夏洛特分校数学与统计系及数据科学学院统计学教授，已发表超过70篇（生物）统计学和金融计量经济学领域的学术论文，长期担任Statistica Sinica和其它数种期刊的副主编，主持美国国家科学基金（NSF）和美国国立卫生研究院（NIH）多个项目。他的研究领域广泛，涵盖人工智能驱动的数学、金融时间序列分析、高维模型的统计推断、生存分析等。目前，他担任北卡罗来纳大学夏洛特分校“可信人工智能模型管理中心”（TAIM2）的联合首席研究员，致力于推动可信人工智能的研究与应用。