讲座题目：Low-rank optimization models for spectral compressed sensing

主讲人：杨在 教授

讲座时间：5月18日(星期三)下午15:00-16：30

#腾讯会议：945-934-560

个人简介：

杨在，西安交通大学数学与统计学院教授、博士生导师。2007和2009年分获中山大学应用数学专业本科和硕士学位，2014年获新加坡南洋理工大学电子电气工程专业博士学位。主要从事信息处理与无线通信的数学理论与方法研究，发表学术论文40余篇，被引用2700余次（谷歌学术）。任2017年欧洲信号处理会议Tutorial主讲人、IEEE高级会员和Signal Processing期刊编委。2019年获基金委优秀青年基金资助。

https://gr.xjtu.edu.cn/web/yangzai/home

报告摘要：

Spectral compressed sensing refers to the recovery of a spectrally sparse signal from compressive measurements and has wide applications in radar and wireless communications. It is linked to low-rank matrix recovery by applying Kronecker and Carathéodory-Fejér theorems for Hankel and Toeplitz matrices, based on which convex and nonconvex optimization algorithms have been proposed. In this talk, we will introduce previous low-rank matrix recovery formulations and point out their limitations. After that, we present two new low-rank optimization models to resolve these limitations and demonstrate their effectiveness with convex and nonconvex algorithms.