  In this paper, we propose an algorithm referred to as multipath matching
pursuit that investigates multiple promising candidates to recover sparse
signals from compressed measurements. Our method is inspired by the fact that
the problem to find the candidate that minimizes the residual is readily
modeled as a combinatoric tree search problem and the greedy search strategy is
a good fit for solving this problem. In the empirical results as well as the
restricted isometry property (RIP) based performance guarantee, we show that
the proposed MMP algorithm is effective in reconstructing original sparse
signals for both noiseless and noisy scenarios.
