Restricted $p$-isometry property and its application for nonconvex compressive sensing

Details Restricted $p$-isometry property and its application for nonconvex compressive sensing Restricted $p$-isometry property and its application for nonconvex compressive sensing

2010-07-26

arxiv.org/abs/1007.4396v2

Mathematics

Functional Analysis

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Scientific paper

Compressed sensing is a new scheme which shows the ability to recover sparse signal from fewer measurements, using $l_1$ minimization. Recently, Chartrand and Staneva shown in \cite{CS1} that the $l_p$ minimization with $01 - 1 / {N\choose S}$ for $p$ smaller. The second purpose of the paper is to show that under certain weaker conditions, decoders $\triangle_p$ are stable in the sense that they are $(2,p)$ instance optimal for a large class of encoder for $0

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