We address the problem of finding sparse solutions to an underdetermined system of
equations when there are multiple measurement vectors having the same, but unknown …

Finding sparse solutions to underdetermined inverse problems is a fundamental challenge
encountered in a wide range of signal processing applications, from signal acquisition to …

We demonstrate a simple greedy algorithm that can reliably recover a vector v¿¿ d from
incomplete and inaccurate measurements x=¿ v+ e. Here,¿ is a N xd measurement matrix …

This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery
from an incomplete set of linear measurements—L 1-minimization methods and iterative …

In this paper, we address compressed sensing of a low-rank matrix posing the inverse
problem as an approximation problem with a specified target rank of the solution. A simple …

Greedy algorithms are often used to solve under-determined inverse problems when the
solution is constrained to be sparse, ie the solution is only expected to have a relatively …

Parameter estimation from multiple measurement vectors (MMVs) is a fundamental problem
in many signal processing applications, eg, spectral analysis and direction-of-arrival …

The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP).
BP finds the least ℓ 1-norm solution of the underdetermined linear system Ax= b and is used …

The joint-sparse recovery problem aims to recover, from sets of compressed measurements,
unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an …