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：2018年10月26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