Gradient-Type Algorithms for Partial Singular Value Decomposition

Raziel Haimi Cohen, Arnon Cohen

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


It is often desirable to calculate only a few terms of the SVD expansion of a matrix, corresponding to the largest or smallest singular values. Two algorithms, based on gradient and conjugate gradient search, are proposed for this purpose. SVD is computed term by term in a decreasing or increasing order of singular values. The algorithms are simple to implement and are especially advantageous with large matrices.

Original languageEnglish
Pages (from-to)137-142
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number1
StatePublished - 1 Jan 1987


  • Conjugate gradient
  • Rayleigh quotient
  • gradient search
  • partial singular
  • value decomposition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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