Sparse Signal Estimation by Maximally Sparse Convex Optimization
Sparse Signal Estimation by Maximally Sparse Convex Optimization
This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g. sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) …