Projected Subgradient Method under Sparsity Constraints
题目：Projected Subgradient Method under Sparsity Constraints
报告人：沈益 (浙江理工大学)
时间：2019.04.26(周五)下午3:00
地点：紫金港校区管理学院行政楼14楼1417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 talk 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. Then we apply the projected subgradient method to some related non-convex optimization models arising in compressive sensing with l1-constraint, sparse support vector machines.
欢迎广大师生踊跃参加!
浙江大学数据科学研究中心
报告人简介：
沈益，男，教授，浙江省中青年学科带头人。2009年毕业于浙江大学数学系获博士学位。2011年5月任职于浙江理工大学数学系。2012年获得加拿大Pacific Institute for the Mathematical Sciences资助，于2012-2014期间分别在加拿大Alberta大学和加拿大Calgary大学从事博士后研究工作。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、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。