Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6422606 | Journal of Computational and Applied Mathematics | 2014 | 13 Pages |
Abstract
In this paper, we propose a reweighted nuclear norm minimization algorithm based on the weighted fixed point method (RNNM-WFP algorithm) to recover a low rank matrix, which iteratively solves an unconstrained L2-Mp minimization problem introduced as a nonconvex smooth approximation of the low rank matrix minimization problem. We prove that any accumulation point of the sequence generated by the RNNM-WFP algorithm is a stationary point of the L2-Mp minimization problem. Numerical experiments on randomly generated matrix completion problems indicate that the proposed algorithm has better recoverability compared to existing iteratively reweighted algorithms.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Yu-Fan Li, Yan-Jiao Zhang, Zheng-Hai Huang,