A reweighted nuclear norm minimization algorithm for low rank matrix recovery

作者：Li Yu Fan; Zhang Yan Jiao; Huang Zheng Hai^*

来源：Journal of Computational and Applied Mathematics, 2014, 263: 338-350.

DOI：10.1016/j.cam.2013.12.005

• Low rank matrix minimization
• Matrix completion problem
• Reweighted nuclear norm minimization
• Weighted fixed point method

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 L-2-M-p 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 L-2-M-p minimization problem. Numerical experiments on randomly generated matrix completion problems indicate that the proposed algorithm has better recoverability compared to existing iteratively reweighted algorithms.

• 出版日期2014-6
• 单位天津大学