报告题目：Randomized Orthogonal Matching Pursuit Algorithm with Adaptive Partial Selection for Sparse Signal Recovery

报 告 人：温金明 教授（吉林大学）

报告时间：2025年8月27日（星期三）9：00

报告地点：数学科学学院114（小报告厅）

校内联系人：李崇君 教授        联系方式：84708351-8310

报告摘要：The orthogonal matching pursuit (OMP) algorithm, known for its exceptional ability to reconstruct sparse signals, is a widely employed algorithm in compressed sensing. Numerous studies have provided theoretical analyses supporting its capability for achieving exact recovery. However, when applied to large-scale sparse signal recovery, the OMP algorithm incurs substantial computational overhead, leading to prolonged running time. To address this challenge, in this talk, we will introduce a Randomized OMP with Adaptive Partial Selection (AROMP) algorithm to mitigate computational overhead and reduce runtime. The novelty of the AROMP algorithm lies in its utilization of a randomized index selection method rather than a greedy approach to select the index in each iteration. Subsequently, we will theoretically characterize the gap between AROMP and OMP for exactly recovering an s-sparse signal and show that the gap decreases as the number of comparisons K increases, sparsity s decreases, or signal dimension n decreases. We will also show some experimental results to illustrate the efficiency and effectiveness of our proposed method on sparse signal recovery, face recognition tasks, and image reconstruction tasks.

报告人简介：温金明，吉林大学教授、博士生导师、国家青年人才、广东省青年珠江学者，2015年2月博士毕业于加拿大麦吉尔大学，2015年3月至2025年3月先后在法国里昂并行计算实验室、加拿大阿尔伯塔大学、加拿大多伦多大学、暨南大学工作；现任中国数学会理事、广东省计算数学学会常务理事、广东省运筹学会常务理事、IEEE Trans. Audio Speech Lang. Process.、Alex. Eng. J.、《人工智能科学与工程》等期刊编辑，近年来主持国家自然科学基金3项、省级项目4项。温教授的研究方向是整数信号和稀疏信号恢复的算法设计与理论分析，近年来以第一作者/通讯作者在IEEE Trans. Inf. Theory、IEEE Trans. Signal Process.、IEEE/ACM Trans. Audio Speech Lang. Process.、ACM Trans. Asian Low-Resour. Lang. Inf. Process、SIAM J. Imaging Sci.、Inverse Probl.、Appl. Comput. Harmon. Anal.等期刊发表60余篇学术论文，以第一发明人授权中国发明专利13件。