An Alternating l1 approach to the compressed sensing problem

An improvement of the standard $l_1$ relaxation is proposed for the Compressed Sensing problem. Lagrangian duality is used in order to produce a dual approach to the original combinatorial problem of sparsest recovery. We deduce from this approach a practical alternating maximization method and provide preliminary computational experiments showing that the proposed method outperforms the $l_1$ relaxation.