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• W2953227137 abstract "We propose an iterative algorithm for the minimization of a $ell_1$-norm penalized least squares functional, under additional linear constraints. The algorithm is fully explicit: it uses only matrix multiplications with the three matrices present in the problem (in the linear constraint, in the data misfit part and in penalty term of the functional). None of the three matrices must be invertible. Convergence is proven in a finite-dimensional setting. We apply the algorithm to a synthetic problem in magneto-encephalography where it is used for the reconstruction of divergence-free current densities subject to a sparsity promoting penalty on the wavelet coefficients of the current densities. We discuss the effects of imposing zero divergence and of imposing joint sparsity (of the vector components of the current density) on the current density reconstruction." @default.
• W2953227137 created "2019-06-27" @default.
• W2953227137 creator A5007822846 @default.
• W2953227137 creator A5056156306 @default.
• W2953227137 date "2012-02-15" @default.
• W2953227137 modified "2023-09-27" @default.
• W2953227137 title "An iterative algorithm for sparse and constrained recovery with applications to divergence-free current reconstructions in magneto-encephalography" @default.
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