【最优化方向学术报告1】
报告题目：DNNLasso: Scalable Graph Learning for Matrix-Variate Data
报告人：张羊晶 （中国科学院数学与系统科学研究院助理研究员）
邀请人：陈亮
报告地点：数学院425
报告时间： 2024年3月21日 16：00-16：45
报告摘要： We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which
are modelledseparately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the
commonly used matrix-variate Gaussian graphical models, a sparser Kronecker-sum structure was proposed recently based on the Cartesian
product of graphs. However,existing methods for estimating Kronecker-sum structured precision matrices do not scale well to large
scale datasets. In this paper, weintroduce DNNLasso, a diagonally non-negative graphical lasso model for estimating the Kronecker-sum
structured precision matrix, which outperformsthe state-of-the-art methods by a large margin in both accuracy and computational time.
报告人简介：Dr. Yangjing Zhang is currently an assistant professor in Institute of Applied Mathematics, Academy of Mathematics and
Systems Science, Chinese Academy of Sciences. Before Sepetember 2021, she was a research fellow in National University of Singapore.
She obtained the Ph.D degree from National University of Singapore in May 2019, and a B.S. in mathematics from Tsinghua University
in 2014. Her current research is focused on large scale sparse optimization problems, the design of efficient algorithms for
statistical models and graphical models.
