@article{Wang_Cao_Jin_2016, title={Two-Step Proximal Gradient Algorithm for Low-Rank Matrix Completion}, volume={4}, url={http://iapress.org/index.php/soic/article/view/20160608}, DOI={10.19139/soic.v4i2.201}, abstractNote={In this paper, we  propose a two-step proximal gradient algorithm to solve nuclear norm regularized least squares for the purpose of recovering low-rank data matrix from sampling of its entries. Each iteration generated by the proposed algorithm is a combination of the latest three points, namely, the previous point, the current iterate, and its proximal gradient point. This algorithm preserves the computational simplicity of classical proximal gradient algorithm where a singular value decomposition in proximal operator is involved. Global convergence is followed directly in the literature. Numerical results are reported to show the efficiency of the algorithm.}, number={2}, journal={Statistics, Optimization & Information Computing}, author={Wang, Qiuyu and Cao, Wenjiao and Jin, Zhengfen}, year={2016}, month={Jun.}, pages={174-182} }