Matrix completion via a low rank factorization model and an Augmented Lagrangean Succesive Overrelaxation Algorithm

Matrix completion via a low rank factorization model and an Augmented Lagrangean Succesive Overrelaxation Algorithm

The matrix completion problem (MC) has been approximated by using the nuclear norm relaxation. Some algorithms based on this strategy require the computationally expensive singular value decomposition (SVD) at each iteration. One way to avoid SVD calculations is to use alternating methods, which p...

Full description

Bibliographic Details
Main Authors: Hugo Lara, Harry Oviedo, Jinjun Yuan
Format: Article
Language:English
Published: Universidad Simón Bolívar 2015-02-01
Series:Bulletin of Computational Applied Mathematics
Subjects:
matrix completion
alternanting minimization
nonlinear Gauss-Seidel method
nonlinear SOR method
Augmented Lagrange method
Online Access:http://drive.google.com/open?id=0B5GyVVQ6O030bEVPb3owckh5YVE

Internet

http://drive.google.com/open?id=0B5GyVVQ6O030bEVPb3owckh5YVE

Similar Items