A New Hybrid Conjugate Gradient Method Combining BA and PRP Variants
Global Convergence Analysis and Numerical Performance
DOI:
https://doi.org/10.19139/soic-2310-5070-3898Keywords:
Hybrid Nonlinear Conjugate Gradient, Strong Wolfe Line Search, Python.Abstract
This paper introduces a new hybrid nonlinear conjugate gradient algorithm for solving unconstrained optimization problems, based on a convex combination of the AlBayati–AlAssady and Polak-Ribiere-Polyak methods, in which the convex parameter independently satisfies the conjugacy condition. Through rigorous theoretical analysis, this new algorithm guarantees sufficient descent and global convergence properties under the strong Wolfe line search criteria. We tested our method on 21 benchmark unconstrained problems and used Dolan–Moré performance profiles to compare it with classical methods. The results show that the proposed NNR algorithm consistently outperforms the standard BA, PRP, and DY conjugate gradient methods.Downloads
Published
2026-09-16
How to Cite
Rezgui, N., Sellami, N., & Mellal, R. (2026). A New Hybrid Conjugate Gradient Method Combining BA and PRP Variants: Global Convergence Analysis and Numerical Performance. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3898
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Copyright (c) 2026 Nassima Rezgui, Nabil Sellami, Romaissa Mellal
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