Title: Mix Sparse Optimization: Theory and Applications
Time:November 5, 2019 14:30-15:30
Location:X2511
Abstract:Inferring gene regulatory networks from gene expression data is an arduous challenge in biology especially in higher organisms, which is found to have a special structure of transcriptional factory in [Science 2010] and [Nature 2010]. In this talk, we will consider a mix sparse optimization model with this special structure, that is, sparsity structures at intra-group and inter-group levels are considered simultaneously. We will propose a nonconvex regularization method, as well as a first-order iterative algorithm, and present its consistence theory and convergence theory. The applications of mix sparse optimization will facilitate biologists to study the transcriptional factory structure of gene regulation networks for higher model organisms in a genome-wide scale.
Reporter Introduction:Yaohua Hu received his B.S. in Mathematics and M.S. in Computational Mathematics from Zhejiang University in 2007 and 2009, respectively, and Ph.D. in Applied Mathematics from Hong Kong Polytechnic University in 2013. Then he continued his research work in Hong Kong Polytechnic University and Zhejiang University as a postdoctoral fellow. Yaohua Hu joined College of Mathematics and Statistics at Shenzhen University in May 2015, and now he is an associate professor there. His current research interests include theory and algorithms for large-scale optimization, and their various applications in statistics, machine learning, bioinformatics, and image processing.
