Most of the existing sparse-recovery methods assume a static system: the signal is a finite-
length vector for which a fixed set of measurements and sparse representation are available …

To recover a sparse signal from an underdetermined system, we often solve a constrained
ℓ_1-norm minimization problem. In many cases, the signal sparsity and recovery …

Recovery of sparse signals from noisy observations is a problem that arises in many
information processing contexts. LASSO and the Dantzig selector (DS) are two well-known …

Sparse signal restoration is usually formulated as the minimization of a quadratic cost
function| y-Ax|| 2 2 where\mbi A is a dictionary and\mbi x is an unknown sparse vector. It is …

Many signal processing applications revolve around finding a sparse solution to a (often
underdetermined) system of linear equations. Recent results in compressive sensing (CS) …

Sparse recovery aims to reconstruct sparse signals from compressed linear measurements.
In this paper, we propose a sparse recovery algorithm called multiple orthogonal least …

In this paper, we consider the L 1/L 2 minimization for sparse recovery and study its
relationship with the L 1-αL 2 model. Based on this relationship, we propose three numerical …

It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what
appear to be highly incomplete sets of linear measurements and (2) that this can be done by …

The ratio of the L_1 and L_2 norms, denoted by L_1/L_2, becomes attractive due to its scale-
invariant property when approximating the L_0 norm to promote sparsity. In this paper, we …

In this paper, we address recovery of sparse signals from compressed measurements, and
sparse signal approximation, which have received considerable attention over the last …