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- W2223059112 abstract "The minimisation of a quadratic criterion penalised by the l 1 norm of the unknown vector is a classical approach to sparse representations. For real-valued variables, optimisation can be tackled efficiently by quadratic programming. The case of a complex-valued vector is more tricky. It is studied here, with a view to important applications such as spectral analysis. We examine two optimisation strategies, namely Iterative Coordinate Descent (ICD) and Iterative Reweighted Least-Squares (IRLS). Then, we propose an hybrid algorithm that benefits from the specificities of ICD and IRLS. The resulting procedure yields much better performances than Second-Order Cone Programming based algorithms, that were recently proposed for such a problem." @default.
- W2223059112 created "2016-06-24" @default.
- W2223059112 creator A5025864759 @default.
- W2223059112 creator A5062028668 @default.
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- W2223059112 date "2007-09-11" @default.
- W2223059112 modified "2023-09-27" @default.
- W2223059112 title "Minimisation de critères de moindres carrés pénalisés par la norme l1 dans le cas complexe" @default.
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- W2223059112 hasPublicationYear "2007" @default.
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