一、题目：Projected Subgradient Method under Sparsity Constraints

二、主讲人：浙江理工大学 沈益 教授

三、时间：5月8日(周三)上午9：30开始
四、地点：A4-305报告厅

摘要：One-bit compressive sensing theory shows that sparse signals can be almost exactly reconstructed from a small number of one-bit quantized linear measurements. This paper presents the convergence analysis of the binary iterative hard thresholding (BIHT) algorithm which is a state-of-the-art recovery algorithm in one-bit compressive

sensing. The basic idea of the convergence analysis is to view BIHT as a kind of projected subgradient method under sparsity constrains. To the best of our knowledge, this is the first convergence analysis of BIHT. We consider a general convex function subject to sparsity constraints and connect it with the non-convex model in one-bit compressive sensing literatures. A projected subgradient method is proposed to solve the general model and some convergence results are established.

报告人简介：沈益，浙江理工大学教授，浙江省中青年学科带头人。从事小波分析及应用、压缩感知等相关领域的研究。主持国家自然科学基金面上项目、联合基金项目、浙江省杰出青年基金项目等七项省部级项目。在《Applied and Computational Harmonic Analysis》、《IEEE Transaction on Information Theory》和《IEEE Transactions on Signal Processing》等期刊发表SCI论文20篇。