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The Nonnegative $l_0$ Norm Minimization under Generalized $Z$-matrix Measurement

Ziyan Luo(starkeynature***at***hotmail.com)
Linxia Qin(lxqin.echo***at***163.com)
Lingchen Kong(konglchen***at***126.com)
Naihua Xiu(nhxiu***at***bjtu.edu.cn)

Abstract: In this paper, we consider the $l_0$ norm minimization problem with linear equation and nonnegativity constraints. By introducing the concept of generalized $Z$-matrix for a rectangular matrix, we show that this $l_0$ norm minimization with such a kind of measurement matrices and nonnegative observations can be exactly solved via the corresponding $l_p$ ($0

Keywords: Nonnegative $l_0$ norm minimization, generalized $Z$-matrix, $k$-sparse solution, sample number

Category 1: Linear, Cone and Semidefinite Programming

Citation: Beijing Jiaotong University, No.3 Shang Yuan Cun, Hai Dian District Beijing, China. June 2012

Download: [PDF]

Entry Submitted: 06/26/2012
Entry Accepted: 06/26/2012
Entry Last Modified: 06/26/2012

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