  We present a novel stagewise strategy for improving greedy algorithms for
sparse recovery. We demonstrate its efficiency both for synthesis and analysis
sparse priors, where in both cases we demonstrate its computational efficiency
and competitive reconstruction accuracy. In the synthesis case, we also provide
theoretical guarantees for the signal recovery that are on par with the
existing perfect reconstruction bounds for the relaxation-based solvers and
other sophisticated greedy algorithms.
