  We analyze an Iteratively Re-weighted Least Squares (IRLS) algorithm for
promoting l1-minimization in sparse and compressible vector recovery. We prove
its convergence and we estimate its local rate. We show how the algorithm can
be modified in order to promote lt-minimization for t<1, and how this
modification produces superlinear rates of convergence.
