  Sparse signal recovery from a small number of random measurements is a well
known NP-hard to solve combinatorial optimization problem, with important
applications in signal and image processing. The standard approach to the
sparse signal recovery problem is based on the basis pursuit method. This
approach requires the solution of a large convex optimization problem, and
therefore suffers from high computational complexity. Here, we discuss a
stochastic optimization method, as a low-complexity alternative to the basis
pursuit approach.
