Event Detail

Event Type: 
Applied Mathematics and Computation Seminar
Friday, October 14, 2022 - 12:00 to 12:50
STAG 111 and ZOOM

Speaker Info

University of Texas at Dallas

Motivated by re-weighted l_1 approaches for sparse recovery, we propose a lifted l_1 (LL1) regularization that can be generalize to several popular regularizations in the literature. During the course of reformulating the existing methods into our framework, we discover two types of lifting functions that can guarantee that the proposed approach is equivalent to the `0 minimization. Computationally, we design an efficient algorithm via the alternating direction method of multiplier (ADMM) and establish the convergence for an unconstrained formulation. Experimental results are presented to demonstrate how this generalization improves sparse recovery over the state-of-the-art. This is a joint work with Yaghoub Rahimi and Sung Ha Kang from the Georgia Institute of Technology.

BIO: Yifei Lou is an Associate Professor in the Mathematical Sciences Department, University of Texas Dallas, where she has been since 2014. She received her Ph.D. in Applied Math from the University of California Los Angeles (UCLA) in 2010. After graduation, she was a postdoctoral fellow at the School of Electrical and Computer Engineering Georgia Institute of Technology, followed by another postdoc training at the Department of Mathematics, University of California Irvine from 2012-2014. Dr. Lou received the National Science Foundation CAREER Award in 2019. Her research interests include compressive sensing and its applications, image analysis (medical imaging, hyperspectral, imaging through turbulence), and (nonconvex) optimization algorithms.