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abstract:
We present an efficient method to regularize ill-posed problems with sparsity as prior knowledge. We combine mollification methods with standard techniques of optimization to derive an efficient algorithm for solving inverse problems with L1- constrains. Numerical tests illustrate the robustness of the algorithm. |
