By Topic

Parallel GPU implementation of null space based alternating optimization algorithm for large-scale matrix rank minimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Katsumi Konishi ; Department of Computer Science, Kogakuin University, Tokyo Japan

This paper provides an alternating optimization algorithm for large-scale matrix rank minimization problems and its parallel implementation on GPU. The matrix rank minimization problem has a lot of important applications in signal processing, and several useful algorithms have been proposed. However most algorithms cannot be applied to a large-scale problem because of high computational cost. This paper proposes a null space based algorithm, which provides a low-rank solution without computing inverse matrix nor singular value decomposition. The algorithm can be parallelized easily without any approximation and can be applied to a large-scale problem. Numerical examples show that the algorithm provides a low-rank solution efficiently and can be speed up by parallel GPU computing.

Published in:

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

25-30 March 2012