报告人：晁越(苏州大学)

时间：2023年12月01日 16:00-

腾讯会议ID：269 817 150

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.

简介：晁越, 苏州大学统计学在读博士生。研究兴趣包括海量数据分析, 分布式学习, 统计优化理论. 在Information Sciences、Journal of Statistical Computation and simulation 、Metrika等期刊发表学术论文.

邀请人：夏小超

欢迎广大师生积极参与！