Research Article Open Access

A New Spectral Conjugate Gradient Method for Nonlinear Unconstrained Optimization

Ibtisam A. Masmali1, Zabidin Salleh2 and Ahmad Alhawarat2
  • 1 Jazan University, Saudi Arabia
  • 2 University Malaysia Terengganu, Malaysia

Abstract

Theconjugate gradient method is widely used to solve large scale unconstrainedoptimization problems. However, the rate of convergence conjugate gradient method is linearunless it restarted. In this study, we present a new spectral conjugategradient modification formula with restart property obtains the globalconvergence and descent properties.In addition, we proposed a new restart condition for Fletcher-Reeves conjugate gradient formula. The numerical resultsdemonstrated that the modified Fletcher-Reeves parameter and the new CG formulawith their restart conditions are more efficient and robustness than otherconventional methods.

Journal of Computer Science
Volume 17 No. 6, 2021, 598-609

DOI: https://doi.org/10.3844/jcssp.2021.598.609

Submitted On: 13 March 2021 Published On: 5 July 2021

How to Cite: Masmali, I. A., Salleh, Z. & Alhawarat, A. (2021). A New Spectral Conjugate Gradient Method for Nonlinear Unconstrained Optimization. Journal of Computer Science, 17(6), 598-609. https://doi.org/10.3844/jcssp.2021.598.609

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Keywords

  • Conjugate Gradient
  • Global Convergence
  • Descent Condition