On computing multi-dimensional extreme eigen and singular subspaces

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The problem of finding extreme eigenvalues and eigenvectors of a real symmetric positive definite matrix can be viewed as a smooth optimization problem on a smooth manifold. We present a cost function approach for computing higher dimensional subspaces corresponding to smallest and largest eigenvalues simultaneously. This approach is then generalized to develop dynamical system for computing the singular value spread of any real matrix.

Original languageEnglish (US)
Title of host publicationISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems
Subtitle of host publicationNano-Bio Circuit Fabrics and Systems
Pages2570-2573
Number of pages4
DOIs
StatePublished - Aug 31 2010
Event2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010 - Paris, France
Duration: May 30 2010Jun 2 2010

Publication series

NameISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems

Other

Other2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010
CountryFrance
CityParis
Period5/30/106/2/10

Keywords

  • Eigenvalue spread
  • Gradient dynamical systems
  • Joint PCA-MCA
  • Joint PSA-MSA
  • Oja's rule
  • Stiefel manifold

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