A survey of sufficient descent conjugate gradient methods for unconstrained optimization

Yasushi Narushima, Hiroshi Yabe

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this decade, nonlinear conjugate gradient methods have been focused on as effective numerical methods for solving large-scale unconstrained optimization problems. Especially, nonlinear conjugate gradient methods with the sufficient descent property have been studied by many researchers. In this paper, we review sufficient descent nonlinear conjugate gradient methods.

Original languageEnglish
Pages (from-to)167-203
Number of pages37
JournalSUT Journal of Mathematics
Volume50
Issue number2
Publication statusPublished - 2014 Jan 1
Externally publishedYes

Fingerprint

Unconstrained Optimization
Conjugate Gradient Method
Descent
Sufficient
Large-scale Optimization
Numerical Methods
Optimization Problem

Keywords

  • Conjugate gradient method
  • Global convergence
  • Sufficient descent condition
  • Unconstrained optimization

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

A survey of sufficient descent conjugate gradient methods for unconstrained optimization. / Narushima, Yasushi; Yabe, Hiroshi.

In: SUT Journal of Mathematics, Vol. 50, No. 2, 01.01.2014, p. 167-203.

Research output: Contribution to journalArticle

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