The Improved Bounds of Restricted Isometry Constant for Recovery via l(p)-Minimization
Nonconvex l(p)-minimization with p is an element of (0,1) has been studied recently in the context of compressed sensing. In this paper, we prove that as long as the sensing matrix A is an element of R-mxn times satisfies restricted isometry property with delta(2k) is an element of (0,1), every k-sparse signal x is an element of R-n can be recovered exactly from linear measurement y = Ax via solving some l(p)-minimization problem. In fact, it is shown that p < min{1, 1.0873(1 - delta(2k))}suffices for the exact k-sparse recovery of l(p)-minimization, which improves the existing results greatly.
l(p)-minimization compressed sensing (cs) restricted isometry constant (ric)