Lower bounds for sparse recovery

Abstract

We consider the following k-sparse recovery problem: design an m x n matrix A, such that for any signal x, given Ax we can efficiently recover ^x satisfying x|| ^x||1 [less than or equal to] C min[subscript k]-sparse x'||x - x'||1. It is known that there exist matrices A with this property that have only O(k log(n=k)) rows. In this paper we show that this bound is tight. Our bound holds even for the more general random- ized version of the problem, where A is a random variable, and the recovery algorithm is required to work for any fixed x with constant probability (over A).