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An iteratively reweighted least squares algorithm for sparse regularization. (English) Zbl 1392.65074

Cwikel, Michael (ed.) et al., Functional analysis, harmonic analysis, and image processing: a collection of papers in honor of Björn Jawerth. Providence, RI: American Mathematical Society (AMS) (ISBN 978-1-4704-2836-5/pbk; 978-1-4704-4166-1/ebook). Contemporary Mathematics 693, 391-411 (2017).
Summary: We present a new algorithm and the corresponding convergence analysis for the regularization of linear inverse problems with sparsity constraints, applied to a new generalized sparsity promoting functional. The algorithm is based on the idea of iteratively reweighted least squares, reducing the minimization at every iteration step to that of a functional including only \(\ell _2\)-norms. This amounts to smoothing of the absolute value function that appears in the generalized sparsity promoting penalty we consider, with the smoothing becoming iteratively less pronounced. We demonstrate that the sequence of iterates of our algorithm converges to a limit that minimizes the original functional.
For the entire collection see [Zbl 1378.46003].

MSC:

65F10 Iterative numerical methods for linear systems
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References:

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