A new approach of conditions on δ_(2s)(Φ) for s-sparse recovery  

作　　者：CEN YiGang ZHAO RuiZhen MIAO ZhenJiang CEN LiHui CUI LiHong 

机构地区：[1]School of Computer & Information Technology, Beijing Jiaotong University [2]School of Information Science and Engineering, Central South University [3]Key Laboratory of System Control and Information Processing,Ministry of Education [4]Department of Mathematics, Beijing University of Chemical Technology

出　　处：《Science China(Information Sciences)》2014年第4期103-109,共7页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61272028,61104078,61073079,61273274,60802045);Fundamental Research Funds for the Central Universities of China(Grant Nos.2011JBM223,2013JBZ003),Beijing Natural Science Foundation(Grant Nos.4113075,4123104);Spe-cialized Research Fund for the Doctoral Program of Higher Education of China(Grant Nos.20110162120045,20120009110008);Program for New Century Excellent Talents in University(Grant No.NCET-12-0768);National Key-tech R&D Program of China(Grant No.2012BAH01F03);National Basic Research Program of China(973 Program)(Grant No.2011CB302203);Foundation of Key Laboratory of System Control and Information Processing(Grant No.SCIP2011009)

摘　　要：In this paper, we provide a unified expression to obtain the conditions on the restricted isometry constant δ2s（φ）. These conditions cover the important results proposed by Candes et al. and each of them is a sufficient condition for sparse signal recovery. In the noiseless case, when δ2s（φ） satisfies any one of these conditions, the s-sparse signal can be exactly recovered via （11） constrained minimization.In this paper, we provide a unified expression to obtain the conditions on the restricted isometry constant δ2s（φ）. These conditions cover the important results proposed by Candes et al. and each of them is a sufficient condition for sparse signal recovery. In the noiseless case, when δ2s（φ） satisfies any one of these conditions, the s-sparse signal can be exactly recovered via （11） constrained minimization.

关 键 词：concentration of mutilated vector in kerφ s-largest mutilated vector restricted isometry constantδ2s（φ） exact recovery of s-sparse signals via （ll） maximum value of single variable function 

分 类 号：TN911.7[电子电信—通信与信息系统] TP301.6[电子电信—信息与通信工程]