title: Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays.
abstract: 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 restriction. In this paper, we discuss the l₁ 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 l₁ 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.
authors.affiliation: Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China. lxqin.echo@163.com
authors.forename: Linxia
authors.initials: L
authors.lastname: Qin
authors.forename: Naihua
authors.initials: N
authors.lastname: Xiu
authors.forename: Lingchen
authors.initials: L
authors.lastname: Kong
authors.forename: Yu
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004247:DNA / Q000378:metabolism; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D008962:Models, Theoretical; D008967:Molecular Biology / Q000379:methods; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011382:Programming, Linear; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
chemical_list: D004247:DNA