数学科学系

Department of Mathematical Sciences

Projected Subgradient Method under Sparsity Constraints

科学计算团队系列报告

报告人:沈益 (浙江理工大学)

报告时间:20181026日下午4:30-5:30

报告地点:理科楼A404

邀请人:史作强,包承龙

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. This work is jointed with Liu Dekai and Prof. Li Song