Math @ Duke

Publications [#329099] of Ingrid Daubechies
Papers Published
 Voronin, S; Daubechies, I, An iteratively reweighted least squares algorithm for sparse regularization,
in Contemporary Mathematics, vol. 693
(January, 2017),
pp. 391411, American Mathematical Society, ISBN 9781470428365 [doi]
(last updated on 2019/05/19)
Abstract: © 2017 by the authors. 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 ℓ2norms. 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.


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