A nonmonotone memory gradient method for unconstrained optimization

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2 Citations (Scopus)

Abstract

Memory gradient methods are used for unconstrained optimization, especially large scale problems. They were first proposed by Miele and Cantrell (1969) and Cragg and Levy (1969). Recently Narushima and Yabe (2006) proposed a new memory gradient method which generates a descent search direction for the objective function at every iteration and converges globally to the solution if the Wolfe conditions are satisfied within the line search strategy. In this paper, we propose a nonmonotone memory gradient method based on this work. We show that our method converges globally to the solution. Our numerical results show that the proposed method is efficient for some standard test problems if we choose a parameter included in the method suitably.

Original languageEnglish
Pages (from-to)31-45
Number of pages15
JournalJournal of the Operations Research Society of Japan
Volume50
Issue number1
DOIs
Publication statusPublished - 2007 Mar
Externally publishedYes

Keywords

  • Global convergence
  • Large scale problems
  • Memory gradient method
  • Nonlinear programming
  • Nonmonotone line search
  • Optimization

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research

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