L2/3 regularization: Convergence of iterative thresholding algorithm
•Under some condition, the sequence generated by the L2/3 algorithm converges to a local minimizer of L2/3 regularization.•Under the same conditions, the asymptotical convergence rate of L2/3 algorithm is linear.•Numerical experiments support our theoretical analysis. The L2/3 regularization is a no...
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| Vydáno v: | Journal of visual communication and image representation Ročník 33; s. 350 - 357 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.11.2015
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| Témata: | |
| ISSN: | 1047-3203, 1095-9076 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •Under some condition, the sequence generated by the L2/3 algorithm converges to a local minimizer of L2/3 regularization.•Under the same conditions, the asymptotical convergence rate of L2/3 algorithm is linear.•Numerical experiments support our theoretical analysis.
The L2/3 regularization is a nonconvex and nonsmooth optimization problem. Cao et al. (2013) investigated that the L2/3 regularization is more effective in imaging deconvolution. The convergence issue of the iterative thresholding algorithm of L2/3 regularization problem (the L2/3 algorithm) hasn’t been addressed in Cao et al. (2013). In this paper, we study the convergence of the L2/3 algorithm. As the main result, we show that under certain conditions, the sequence {x(n)} generated by the L2/3 algorithm converges to a local minimizer of L2/3 regularization, and its asymptotical convergence rate is linear. We provide a set of experiments to verify our theoretical assertions and show the performance of the algorithm on sparse signal recovery. The established results provide a theoretical guarantee for a wide range of applications of the algorithm. |
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| ISSN: | 1047-3203 1095-9076 |
| DOI: | 10.1016/j.jvcir.2015.10.007 |