Jing Jia, Jianjun Wang
Abstract
In this paper, we present sufficient conditions in term of the restricted isometry property (RIP) to guarantee perfect recovery of sparse signal in the noiseless case and stable recovery in the noisy case via Lq-minimization, especially for nonconvex case 0 Lq-minimization.
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In this paper, we present sufficient conditions in term of the restricted isometry property (RIP) to guarantee perfect recovery of sparse signal in the noiseless case and stable recovery in the noisy case via Lq-minimization, especially for nonconvex case 0 Lq-minimization.
Key concepts: Restricted isometry property, Signal recovery, Minification, SIGNAL (programming language), Property (philosophy), Mathematics, Isometry (Riemannian geometry), Mathematical optimization