On continuous partial singular value decomposition algorithms

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages840-843
Number of pages4
DOIs
StatePublished - Oct 26 2009
Event2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan, Province of China
Duration: May 24 2009May 27 2009

Publication series

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

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period5/24/095/27/09

Keywords

  • Matrix approximation
  • Principal singular subspace
  • Singular value decomposition

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