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2016 A New Algorithm for Positive Semidefinite Matrix Completion
Fangfang Xu, Peng Pan
J. Appl. Math. 2016: 1-5 (2016). DOI: 10.1155/2016/1659019

Abstract

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.

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Fangfang Xu. Peng Pan. "A New Algorithm for Positive Semidefinite Matrix Completion." J. Appl. Math. 2016 1 - 5, 2016. https://doi.org/10.1155/2016/1659019

Information

Received: 29 June 2016; Accepted: 22 September 2016; Published: 2016
First available in Project Euclid: 17 December 2016

zbMATH: 07037265
MathSciNet: MR3568679
Digital Object Identifier: 10.1155/2016/1659019

Rights: Copyright © 2016 Hindawi

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