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Feasibility Pump Algorithm for Sparse Representation under Laplacian Noise
2019
Mathematical Problems in Engineering
The Feasibility Pump is an effective heuristic method for solving mixed integer optimization programs. In this paper the algorithm is adapted for finding the sparse representation of signals affected by Laplacian noise. Two adaptations of the algorithm, regularized and nonregularized, are proposed, tested, and compared against the regularized least absolute deviation (RLAD) model. The obtained results show that the addition of the regularization factor always improves the algorithm. The
doi:10.1155/2019/5615243
fatcat:vhabkrq5afcnxj5basyj3o3h4m