Efficient iterative thresholding algorithms with functional feedbacks and null space tuning

doi:10.1016/j.sigpro.2021.108199

Efficient iterative thresholding algorithms with functional feedbacks and null space tuning

2021-11-01

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DOI：10.1016/j.sigpro.2021.108199

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An accelerated class of adaptive scheme of iterative thresholding algorithms is studied analytically and empirically. They are based on the feedback mechanism of the null space tuning techniques. The main contribution of this article is the accelerated convergence analysis and proofs with a variable/adaptive index selection and different feedback principles at each iteration. The convergence analysis requires no longer a priori sparsity information s of a signal. It is shown that uniform recovery of all s-sparse signals from given linear measurements can be achieved under reasonable (preconditioned) restricted isometry conditions. Accelerated convergence rate and improved convergence conditions are obtained by selecting an appropriate size of the index support per iteration. The theoretical findings are sufficiently demonstrated and confirmed by extensive numerical experiments. It is also observed that the proposed algorithms have a clearly advantageous balance of efficiency, adaptivity and accuracy compared with all other state-of-the-art greedy iterative algorithms. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Sparse signal

Null space tuning

Thresholding

Feedback

Efficient iterative thresholding algorithms with functional feedbacks and null space tuning

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