摘要
本文研究基于AMP的L_(1/2)正则化方法,采用Belief Propagation算法的思想构造了基于AMP改进的Half阈值迭代算法,并证明所提算法至多需要有限步就能精确估计稀疏向量.通过稀疏信号重建实验,我们验证了几种基于AMP的阈值迭代求解算法的非凸正则化方法具有强的信号重建和相变能力.
In this paper, we study the approximate message passing（AMP） algorithm for L_（1/2） regularization.We propose an improved half iterative thresholding algorithm, which is inspired by belief propagation in graphical model. Further, we study the convergence property of the new algorithm. Through extensive simulations, we show that the iterative thresholding algorithms based on AMP for several nonconvex regularization approaches have strong reconstruction capabilities and high phase transition.
作者
张会
张海
Hui ZHANG Hai ZHANG(School of Mathematics, Northwest University, Xi＇an 710069, China Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China)
出处
《中国科学：信息科学》
CSCD
北大核心
2017年第1期58-72,共15页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:11571011)资助项目
