Greedy signal recovery and uncertainty principles

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 methods (Matching Pursuits). We find a simple regularized version of the Orthogonal Matching Pursuit (ROMP) which has advantages of both approaches: the speed and transparency of OMP and the strong uniform guarantees of the L-1-minimization. Our algorithm ROMP reconstructs a sparse signal in a number of iterations linear in the sparsity, and the reconstruction is exact provided the linear measurements satisfy the Uniform Uncertainty Principle. In the case of inaccurate measurements and approximately sparse signals, the noise level of the recovery is proportional to root log n parallel to e parallel to(2) where e is the error vector.