TY - JOUR AU - Qiuyu Wang AU - Wenjiao Cao AU - Zhengfen Jin PY - 2016/06/01 Y2 - 2024/03/29 TI - Two-Step Proximal Gradient Algorithm for Low-Rank Matrix Completion JF - Statistics, Optimization & Information Computing JA - Stat., optim. inf. comput. VL - 4 IS - 2 SE - Research Articles DO - 10.19139/soic.v4i2.201 UR - http://www.iapress.org/index.php/soic/article/view/20160608 AB - 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. ER -