Proximal iteratively reweighted algorithm for low-rank matrix recovery

This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions. Besides, this study...

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Detalles Bibliográficos
Autores principales: Ma, Chao-Qun, Ren, Yi-Shuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758698/
https://www.ncbi.nlm.nih.gov/pubmed/29367824
http://dx.doi.org/10.1186/s13660-017-1602-x
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author Ma, Chao-Qun
Ren, Yi-Shuai
author_facet Ma, Chao-Qun
Ren, Yi-Shuai
author_sort Ma, Chao-Qun
collection PubMed
description This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions. Besides, this study also has shown that the proximal iteratively reweighted algorithm lessens the objective function value monotonically, and any limit point is a stationary point theoretically.
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spelling pubmed-57586982018-01-22 Proximal iteratively reweighted algorithm for low-rank matrix recovery Ma, Chao-Qun Ren, Yi-Shuai J Inequal Appl Research This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions. Besides, this study also has shown that the proximal iteratively reweighted algorithm lessens the objective function value monotonically, and any limit point is a stationary point theoretically. Springer International Publishing 2018-01-08 2018 /pmc/articles/PMC5758698/ /pubmed/29367824 http://dx.doi.org/10.1186/s13660-017-1602-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Ma, Chao-Qun
Ren, Yi-Shuai
Proximal iteratively reweighted algorithm for low-rank matrix recovery
title Proximal iteratively reweighted algorithm for low-rank matrix recovery
title_full Proximal iteratively reweighted algorithm for low-rank matrix recovery
title_fullStr Proximal iteratively reweighted algorithm for low-rank matrix recovery
title_full_unstemmed Proximal iteratively reweighted algorithm for low-rank matrix recovery
title_short Proximal iteratively reweighted algorithm for low-rank matrix recovery
title_sort proximal iteratively reweighted algorithm for low-rank matrix recovery
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758698/
https://www.ncbi.nlm.nih.gov/pubmed/29367824
http://dx.doi.org/10.1186/s13660-017-1602-x
work_keys_str_mv AT machaoqun proximaliterativelyreweightedalgorithmforlowrankmatrixrecovery
AT renyishuai proximaliterativelyreweightedalgorithmforlowrankmatrixrecovery

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