Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays
type: Item
instance of: scholarly article
title: Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays
zbMATH Open document ID: 1254.92028
DOI: 10.1155/2012/646045
author: Linxia Qin
author: Lingchen Kong
author: Yu Li
author: Naihua Xiu
published in: Computational \& Mathematical Methods in Medicine
publication date: 2013-01-09T00:00:00Z
review text: Summary: Compressive sensing microarrays (CSM) are DNA-based sensors that operate using group testing and compressive sensing principles. Mathematically, one can cast the CSM as sparse nonnegative recovery (SNR) which is to find the sparsest solutions subjected to an underdetermined system of linear equations and nonnegative restrictions. In this paper, we discuss the \(l_1\) relaxation of the SNR. By defining nonnegative restricted isometry/orthogonality constants, we give a nonnegative restricted property condition which guarantees that the SNR and the \(l_1\) relaxation share the common unique solution. Besides, we show that any solution to the SNR must be one of the extreme points of the underlying feasible set.
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 90C05
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 6123557
Wikidata QID: Q36432356
describes a project that uses: PDCO
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/646045
OpenAlex ID: W2096394018
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DBLP publication ID: journals/cmmm/QinXKL12
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description: scientific article; zbMATH DE number 6123557