讲座人简介：

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bv1946伟德，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等期刊和会议发表30余篇学术论文。

讲座简介：

Exact recovery of a sparse signal x from a linear measurements arises from many applications. The orthogonal matching pursuit (OMP) algorithm is a widely used algorithm for reconstructing x. A fundamental question in the performance analysis of OMP is the characterizations of the probability that it can exactly recover x for random sensing matrices and the necessary number of measurements to guarantee a satisfactory recovery performance. Although in many practical applications, in addition to the sparsity, x usually also has some additional properties (for example, the nonzero entries of x independently and identically follow the Gaussian distribution, and x has exponential decaying property), as far as we know, none of existing analysis uses these properties to answer the above question. In this talk, we will use the prior information of x to refine the performance analysis of the OMP algorithm. Specifically, we develop a better lower bound on the probability of exact recovery with OMP and a better lower bound on the necessary number of measurements to guarantee a target recovery performance. The new bounds are significantly better than existing ones. This is joint work with Prof. Wei Yu from Toronto University and Dr. Rui Zhang from Huawei Technologies Company, Ltd.