报告人 ：温金明  （暨南大学 教授）

报告人简介 ：

   温金明，暨南大学教授、博导、青年珠江学者；2015年6月毕业于加拿大麦吉尔大学37000cm威尼斯，获哲学博士学位。从2015年3月到2018年9月，温博士先后在法国科学院里昂并行计算实验室、加拿大阿尔伯塔大学、多伦多大学从事博士后研究工作。他的研究方向主要是整数信号和稀疏信号恢复的算法设计与理论分析。他以第一作者/通讯作者在Applied and Computational Harmonic Analysis、IEEE Transactions on Information Theory、 IEEE Transactions on Signal Processing等顶级期刊和会议发表30余篇学术论文。

报告题目:Signal-Dependent Performance Analysis of OMP

报告摘要：In this talk, we first show that the prior distribution information of $x$ can be used to provide an upper bound on $|x|_1^2/|x|_2^2$. Then, we explore this upper bound to develop a better lower bound on the probability of exact recovery with OMP in $K$ iterations. Furthermore, we develop a lower bound on $m$ to guarantee that the exact recovery probability of $K$ iterations of OMP is not lower than a given probability. We further show that, if $K$ is sufficiently small compared with $n$, when $K$ approaches infinity, $mapprox 2Kln(n)$, $mapprox K$ and $mapprox 1.6Kln(n)$ are enough to ensure that OMP has a satisfactory recovery performance for recovering any $K$-sparse $x$, $K$-sparse $x$ with exponential decaying property and $K$-sparse $x$ whose nonzero entries independently and identically follow the Gaussian distribution, respectively. This significantly improves Tropp {em{et. al.}}'s result which requires $mapprox4Kln(n/delta)$.

报告时间 ：2020年10月19日下午15：00-17：00

报告地点 ：腾讯会议室：795953726