Recovery of sparsest signals via l(q)-minimization
Abbreviated Journal Title
Appl. Comput. Harmon. Anal.
Compressive sampling; l(q)-minimization; Sparse signal; RECONSTRUCTION; MINIMIZATION; Mathematics, Applied; Physics, Mathematical
In this paper, it is proved that every s-sparse vector x is an element of R-n can be exactly recovered from the measurement vector z = Ax is an element of R-m via some l(q)-minimization with 0 < q <= 1, as soon as each s-sparse vector x is an element of R-n is uniquely determined by the measurement z. Moreover it is shown that the exponent q in the l(q)-minimization can be so chosen to be about 0.6796 x (1- delta(25)(A)), where delta(25)(A) is the restricted isometry constant of order 2s for the measurement matrix A. (C) 2011 Elsevier Inc. All rights reserved.
Applied and Computational Harmonic Analysis
"Recovery of sparsest signals via l(q)-minimization" (2012). Faculty Bibliography 2010s. 2532.