On continuous partial singular value decomposition algorithms

Mohammed A Hasan

doi:10.1109/ISCAS.2009.5117887

On continuous partial singular value decomposition algorithms

Mohammed A Hasan

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimization techniques of unconstrained merit functions. The proposed techniques led to computing low-rank matrix approximation by solving nonlinear matrix differential equations. Numerical experiments illustrate the theoretical results.

Original language	English (US)
Title of host publication	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages	840-843
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2009.5117887
State	Published - Oct 26 2009
Event	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan, Province of China Duration: May 24 2009 → May 27 2009

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Other

Other	2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Country	Taiwan, Province of China
City	Taipei
Period	5/24/09 → 5/27/09

Keywords

Matrix approximation
Principal singular subspace
Singular value decomposition

Access

10.1109/ISCAS.2009.5117887

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Hasan, MA 2009, On continuous partial singular value decomposition algorithms. in 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009., 5117887, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 840-843, 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009, Taipei, Taiwan, Province of China, 5/24/09. https://doi.org/10.1109/ISCAS.2009.5117887

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AB - Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimization techniques of unconstrained merit functions. The proposed techniques led to computing low-rank matrix approximation by solving nonlinear matrix differential equations. Numerical experiments illustrate the theoretical results.

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