Updating quasi newton matrices with limited storage

Jorge Nocedal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1829 Scopus citations


We study how to use the BFGS quasi-Newton matrices to precondition minimization methods for problems where the storage is critical.We give an update formula which generates matrices using information from the last m iterations, where m is any number supplied by the user. The quasi-Newton matrix is updated at every iteration by dropping the oldest information and replacing it by the newest information. It is shown that the matrices generated have some desirable properties. The resulting algorithms are tested numerically and compared with several well known methods.

Original languageEnglish (US)
Pages (from-to)951-958
Number of pages8
JournalMathematics of Computation
Issue number151
StatePublished - Jul 1980

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Computational Mathematics
  • Applied Mathematics


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