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Journal of Applied Mathematics
Volume 2016, Article ID 1659019, 5 pages
Research Article

A New Algorithm for Positive Semidefinite Matrix Completion

College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China

Received 29 June 2016; Accepted 22 September 2016

Academic Editor: Qing-Wen Wang

Copyright © 2016 Fangfang Xu and Peng Pan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control. This task can be conducted by solving the nuclear norm regularized linear least squares model with positive semidefinite constraints. We apply the widely used alternating direction method of multipliers to solve the model and get a novel algorithm. The applicability and efficiency of the new algorithm are demonstrated in numerical experiments. Recovery results show that our algorithm is helpful.