Proximal iteratively reweighted algorithm for low-rank matrix recovery

被引：0
|
作者：
Chao-Qun Ma
Yi-Shuai Ren
机构：
[1] Hunan University,Business School
来源：
Journal of Inequalities and Applications | / 2018卷
关键词：
compressed sensing; matrix rank minimization; reweighted nuclear norm minimization; Schatten-; quasi-norm minimization;
D O I：
暂无
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学科分类号：
摘要：
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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