A fast and efficient algorithm for low rank matrix recovery from incomplete observations

摘要

Minimizing the rank of a matrix X over certain constraints arises in diverse areas such as machine learning, control system and is known to be computationally NP-hard. In this paper, a new simple and efficient algorithm for solving this rank minimization problem with linear constraints is proposed. By using gradient projection method to optimize S while consecutively updating matrices U and V (where X = USV