主讲人：罗自炎

活动时间：6月3日

地点：第二报告厅

讲座内容：

Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. Optimization with tensor variables is then widely applied in many fields including high-dimensional statistics, machine learning and artificial intelligence, data mining, signal and image processing, fault diagnosis, etc. With the intrinsic sparsities in real data sets, and the dimensionality reduction demands from applications, sparse tensor optimization (STO) is emerging, and efforts are distributed in a variety of specific applications, such as tensor regression in statistics, tensor recovery in signal processing, support tensor machine and tensor clustering in machine learning, etc. In this talk, we will first give a brief introduction to the tensor algebra, and then various sparsity characterizations of tensors arising from different applications. The models of STO will be presented, and several recent application works in STO of our group will be briefly introduced. Indeed, STO is closely related to and heavily relied on the traditional sparse optimization and low-rank matrix optimization, and the optimization theory and algorithms for STO are still in the early stage. Young researchers will be so welcomed to join in the study of STO.

主讲人介绍：

罗自炎，北京交通大学数学与统计学院教授、博士生导师，中国运筹学会数学规划分会副秘书长，中国运筹学会女性工作委员会委员，中国运筹学会算法软件与应用分会理事。曾访问美国斯坦福大学、新加坡国立大学、香港理工大学、英国南安普顿大学等。主要从事张量优化、稀疏优化及统计优化的理论、算法及应用研究，在SIAM J Optim、Math Program、MOR、IEEE TSP、JMLR等顶级期刊发表学术论文，合著SIAM出版社英文专著1部，获教育部自然科学奖二等奖、中国运筹学会青年科技奖提名奖、北京市高校本科优秀毕业论文指导教师，2023年入选国家级青年人才计划。