Title: Exploring intrinsic structured sparsity in convex composite programming

Speaker：Dr. Meixia Lin, National University of Singapore

Date: 2022.02.12, 14:00-15:00 

腾讯会议：499-260-871

Abstract: 

Convex optimization models have been widely used in many applications such as machine learning and data science. However, the huge computation for the involved potentially large-scale problems has prevented their deployments in resource-limited devices. In our work, we design efficient second-order algorithms for the structured convex composite programming problems, which fully exploit the structure of the data and the underlying Hessians to highly reduce the computational cost. Dimension reduction techniques are also designed to further accelerate the computation, especially for the high-dimensional cases.

Biography: 

Meixia Lin is a research fellow in Institute of Operations Research and Analytics at National University of Singapore. She received her Ph.D. degree from Department of Mathematics, National University of Singapore in 2020, and her B.S. degree from Nanjing University in 2016. Her research interests lie in developing models and algorithms in data science with emphasis on large-scale application problems arising in machine learning, statistical estimations, and operations research.