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A Mixed Hybrid Conjugate Gradient Method for Unconstrained Engineering Optimization Problems

  • David A. Oladepo
  • Olawale J. Adeleke
  • Churchill T. Ako
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)

Abstract

A new hybrid of the conjugate gradient (CG) method that combines the features of five different CG methods is proposed. The corresponding CG algorithm generated descent directions independent of line search procedures. With the standard Wolfe line search conditions, the algorithm was shown to be globally convergent. Based on numerical experiments with selected large-scale benchmark test functions and comparison with classical methods, the method is very promising and competitive.

Keywords

Unconstrained optimization problems Hybrid nonlinear conjugate gradient method Descent direction Global convergence Standard Wolfe line search conditions Numerical experiment 

Notes

Acknowledgments

The support of the DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the CoE. The authors also wish to appreciate Covenant University Centre for Research, Innovation and Discovery (CUCRID) for funding the publication of this research output.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • David A. Oladepo
    • 1
  • Olawale J. Adeleke
    • 2
    • 3
  • Churchill T. Ako
    • 1
  1. 1.Department of Petroleum Engineering, College of EngineeringCovenant UniversityOtaNigeria
  2. 2.Department of Mathematics, College of Science and TechnologyCovenant UniversityOtaNigeria
  3. 3.DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS)JohannesburgSouth Africa

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