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• W2897775965 abstract "Iterative l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> reweighting algorithms are very popular in sparse signal recovery and compressed sensing, since in the practice they have been observed to outperform classical 11 methods. Nevertheless, the theoretical analysis of their convergence is a critical point, and generally is limited to the convergence of the functional to a local minimum or to subsequence convergence. In this letter, we propose a new convergence analysis of a Lasso l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> reweighting method, based on the observation that the algorithm is an alternated convex search for a biconvex problem. Based on that, we are able to prove the numerical convergence of the sequence of the iterates generated by the algorithm. Furthermore, we propose an alternative iterative soft thresholding procedure, which is faster than the main algorithm." @default.
• W2897775965 created "2018-10-26" @default.
• W2897775965 creator A5082948079 @default.
• W2897775965 date "2018-12-01" @default.
• W2897775965 modified "2023-10-01" @default.
• W2897775965 title "A Biconvex Analysis for Lasso <inline-formula> <tex-math notation=LaTeX>$ell _1$</tex-math> </inline-formula> Reweighting" @default.
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• W2897775965 doi "https://doi.org/10.1109/lsp.2018.2875251" @default.
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