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Research Article Open access CC BY 3.0

Weak RIP and Its Application to Compressed Sensing

Hiroshi Inoue

Journal of Advances in Mathematics and Computer Science · pp. 674–684 · Published 3 Dec 2013

10.9734/BJMCS/2014/7298

Abstract

The first purpose of this paper is to give a sufficient condition under which A obeys the weak RIP and to evaluate the solution of CS using this result. The second is to show that when an m x n random matrix A satisfies the isotropy property:  for every row vector A{k} of A,   always obeys the weak RIP with high probability and it is applicable to the CS theory.

Compressed sensing Isotropy property Restricted isometry constants Restricted isometry property Sparse approximation Sparse signal recovery Weak restricted isometry property.

References (12)

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  11. 11 Sufficient Conditions for CS-recovery [DOI]
  12. 12 A generalization of the restricted isometry property and applications to compressed sensing

Cited by 4

New Bounds for Restricted Isometry Constant for the s-sparse Recovery via Compressed Sensing

Hiroshi Inoue · Journal of Advances in Mathematics and Computer Science · 2014

WITHDRAWN: RIPless theory for compressed sensing

Hiroshi Inoue · Applied and Computational Harmonic Analysis · 2014

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