Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

AN IMPROVED HYBRID CONJUGATE GRADIENT METHOD WITH SPECTRAL STRATEGY AND ITS APPLICATIONS IN COVID-19 PREDICTION

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  • Kamilu Kamfa,
  • Rabiu Bashir Yunus,
  • Muhammad Auwal Lawan
Kamilu Kamfa
Department of Mathematics, Faculty of Computing and Mathematical Sciences, Aliko Dangote University of Science and Technology, Nigeria
Rabiu Bashir Yunus
Department of Mathematics, Faculty of Computing and Mathematical Sciences, Aliko Dangote University of Science and Technology, Nigeria
Muhammad Auwal Lawan
Department of Mathematics, Faculty of Computing and Mathematical Sciences, Aliko Dangote University of Science and Technology, Nigeria
DOI: https://doi.org/10.30598/barekengvol19iss4pp2803-2814
Published September 1, 2025
Keywords
  • Algorithm,
  • Conjugate Gradient,
  • Covid-19,
  • Hybrid,
  • Monotone
How to Cite
[1]
K. Kamfa, R. B. Yunus, and M. A. Lawan, “AN IMPROVED HYBRID CONJUGATE GRADIENT METHOD WITH SPECTRAL STRATEGY AND ITS APPLICATIONS IN COVID-19 PREDICTION”, BAREKENG: J. Math. & App., vol. 19, no. 4, pp. 2803-2814, Sep. 2025.

Copyright (c) 2025 Kamilu Kamfa, Rabiu Bashir Yunus, Muhammad Auwal Lawan

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

This paper introduces a hybrid conjugate gradient (CG) method for unconstrained optimization with a spectral strategy, inspired by key advancements in existing CG techniques. The proposed method guarantees a descent direction at every iteration, regardless of the line search scheme employed. Its global convergence is rigorously established under the Wolfe line search conditions. Numerical experiments on benchmark optimization problems demonstrate that the proposed method outperforms the FR and RMIL methods across multiple performance metrics. Furthermore, its effectiveness is showcased in a neural network framework for predicting chickenpox and COVID-19 infection cases, highlighting its practical applicability in real-world scenarios.

 

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