A HYBRID CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION WITH APPLICATION

Nasiru Salihu; Huzaifa Aliyu Babando; Ibrahim Arzuka; Suraj Salihu

doi:10.58715/bangmodjmcs.2023.9.3

A HYBRID CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION WITH APPLICATION

Authors

Nasiru Salihu King Mongkhut's University of Technology Thonburi
Huzaifa Aliyu Babando Department of Mathematics, Faculty of Sciences, Modibbo Adama University, Yola, Nigeria
Ibrahim Arzuka Department of Mathematics, Bauchi State University, Gadau, Nigeria
Suraj Salihu Department of Computer Science, Gombe State University, Nigeria

DOI:

https://doi.org/10.58715/bangmodjmcs.2023.9.3

Keywords:

Unconstrained optimization; Hybrid conjugate gradient method; Sufficient descent; Global convergence; Motion control

Abstract

This article considers a hybrid minimization algorithm from optimal choice of the modulating
non-negative parameter of Dai-Liao conjugacy condition. The new hybrid parameter is selected in such
away that a convex combination of Hestenes-Stiefel and Dai-Yuan Conjugate Gradient (CG) algorithms
is fulfilled. The numerical implementation adopts inexact line search which reveals that the scheme is
robust when compared with some known efficient algorithms in literature. Furthermore, the theoretical
analysis shows that the proposed hybrid method converges globally. The method is also applicable to
solve three degree of freedom motion control robotic model.

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Published

2023-12-31

How to Cite

Salihu, N., Aliyu Babando, H., Arzuka, I., & Salihu, S. (2023). A HYBRID CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION WITH APPLICATION. Bangmod International Journal of Mathematical and Computational Science, 9, 24–44. https://doi.org/10.58715/bangmodjmcs.2023.9.3