A note on guaranteed sparse recovery via l(1)-minimization Academic Article

abstract

• It is proved that every s-sparse vector xN can be recovered from the measurement vector y=Axm via 1-minimization as soon as the 2s-th restricted isometry constant of the matrix A is smaller than 3/(4+6)0.4652, or smaller than 4/(6+6)0.4734 for large values of s. 2009 Elsevier Ltd. All rights reserved.

authors

• Foucart, Simon

published proceedings

• APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS

author list (cited authors)

• Foucart, S.

citation count

• 123

complete list of authors

• Foucart, Simon

publication date

• January 2010

publisher

• Elsevier  Publisher

published in

• Applied and Computational Harmonic Analysis  Journal

keywords

• Compressive Sensing
• L(1)-minimization
• Restricted Isometry Constants

Digital Object Identifier (DOI)

• 10.1016/j.acha.2009.10.004

start page

• 97

end page

• 103

volume

• 29

issue

• 1

URL

• http://dx.doi.org/10.1016/j.acha.2009.10.004