Rewighted L1-minimization for sparse solutions to underdetermined linear systems

摘要

We proposed a simple and efficient iteratively reweighted algorithm with iterative support set to improve the recover performance for compressive sensing (CS). The numerical experiential results demonstrate that the new method outperforms in successful probabilities, compared with classical l1 -minimization and other iteratively reweighted l1 -algorithms.