A new spectral conjugate gradient method with descent condition and global convergence property for unconstrained optimization

Maulana Malik, Mustafa Mamat, Siti S. Abas, Ibrahim M. Sulaiman, Sukono

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The Spectral conjugate gradient method is an efficient method for solving large-scale unconstrained optimization problems. In this paper, we propose a new spectral conjugate gradient method in which performance is analyzed numerically. We establish the descent condition and global convergence property under some assump-tions and the strong Wolfe line search. Numerical experiments to evaluate the method’s efficiency are conducted using 98 problems with various dimensions and initial points. The numerical results based on the number of iterations and central processing unit time show that the new method has a high performance computational.

Original languageEnglish
Pages (from-to)2053-2069
Number of pages17
JournalJournal of Mathematical and Computational Science
Volume10
Issue number5
DOIs
Publication statusPublished - 2020

Keywords

  • Descent condition
  • Global convergence property
  • Spectral conjugate gradient method
  • Strong Wolfe line search
  • Unconstrained optimization

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