报告人：马学俊副教授 苏州大学

时间：2023年12月8日上午10:00

地点：数学与信息学院数学系605  

报告摘要：With the availability of extraordinarily huge data sets, solving the problems of distributed statistical methodology and computing for such data sets has become increasingly crucial in the big data area. In this paper, we focus on the distributed sparse penalized linear log-contrast model in massive compositional data. In particular, two distributed optimization techniques under centralized and decentralized topologies are proposed for solving the two different constrained convex optimization problems. Both two proposed algorithms are based on the frameworks of Alternating Direction Method of Multipliers (ADMM) and Coordinate Descent Method of Multipliers(CDMM, Lin et al., 2014, Biometrika). It is worth emphasizing that, in the decentralized topology, we introduce a distributed coordinate-wise descent algorithm based on Group ADMM(GADMM, Elgabli et al., 2020, Journal of Machine Learning Research) for obtaining a communication-efficient regularized estimation. Correspondingly, the convergence theories of the proposed algorithms are rigorously established under some regularity conditions. Numerical experiments on both synthetic and real data are conducted to evaluate our proposed algorithms.
报告人简介：马学俊，苏州大学数学科学学院长聘副教授，博士生导师。博士毕业于中国人民大学统计学院，新加坡国立大学博士后，苏州大学优秀青年学者称号。主持国家级和省部级项目3项，目前主要从事海量数据分析、统计计算、非参数回归等统计模型及其应用研究。目前已经在国内外权威期刊Information Sciences、Journal of Multivariate Analysis、新华文摘等发表论文20余篇。

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