American Institute of Mathematical Sciences

October  2016, 12(4): 1507-1519. doi: 10.3934/jimo.2016.12.1507

On linear convergence of projected gradient method for a class of affine rank minimization problems

 1 School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China 2 Business School and China Academy of Corporate Governance, Nankai University, Tianjin 300071, China

Received  June 2015 Revised  October 2015 Published  January 2016

The affine rank minimization problem is to find a low-rank matrix satisfying a set of linear equations, which includes the well-known matrix completion problem as a special case and draws much attention in recent years. In this paper, a new model for affine rank minimization problem is proposed. The new model not only enhances the robustness of affine rank minimization problem, but also leads to high nonconvexity. We show that if the classical projected gradient method is applied to solve our new model, the linear convergence rate can be established under some conditions. Some preliminary experiments have been conducted to show the efficiency and effectiveness of our method.
Citation: Yu-Ning Yang, Su Zhang. On linear convergence of projected gradient method for a class of affine rank minimization problems. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1507-1519. doi: 10.3934/jimo.2016.12.1507
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