A New Algorithm for Positive Semidefinite Matrix Completion
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-25
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xu, Fangfang& Pan, Peng. 2016. A New Algorithm for Positive Semidefinite Matrix Completion. Journal of Applied Mathematics،Vol. 2016, no. 2016, pp.1-5.
https://search.emarefa.net/detail/BIM-1107186
Modern Language Association (MLA)
Xu, Fangfang& Pan, Peng. A New Algorithm for Positive Semidefinite Matrix Completion. Journal of Applied Mathematics No. 2016 (2016), pp.1-5.
https://search.emarefa.net/detail/BIM-1107186
American Medical Association (AMA)
Xu, Fangfang& Pan, Peng. A New Algorithm for Positive Semidefinite Matrix Completion. Journal of Applied Mathematics. 2016. Vol. 2016, no. 2016, pp.1-5.
https://search.emarefa.net/detail/BIM-1107186
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1107186