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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… Click to show full abstract
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.
Keywords:
reweighted algorithm;
iteratively reweighted;
rank matrix;
low rank;
proximal iteratively
Journal Title: Journal of Inequalities and Applications
Year Published: 2018
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Journal Title: Journal of Inequalities and Applications
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!