An improved hybrid nonlinear conjugate gradient method and application to image restoration problems

Authors

  • Mehamdia Abd Elhamid Department of Mathematics, Universite M’Hamed Bougara of Boumerdes, Algeria
  • Issam A. R. Moghrabi Departement of Information Systems and Technology, Kuwait Technical College, Kuwait
  • Basim A. Hassan College of Computers Sciences and Mathematics, University of Mosul, Iraq
  • Nivine Guler Departement of Articial Intelligence, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Alkhobar, Saudi Arabia

DOI:

https://doi.org/10.19139/soic-2310-5070-3366

Keywords:

Hybrid conjugate gradient method, Inexact line search, Descent condition, Global convergence, Numerical comparisons

Abstract

Optimization methods are widely used to obtain the numerical solution of the optimal control problems arising in scientific and engineering computation, especially for solving large-scale problems. In this paper, based on some modern and computationally efficient methods, a new conjugate gradient method ( named ICG method) is proposed for unconstrained optimization. Under the strong Wolfe line search (SWLS), the presented method is proven to be sufficient descent at each iteration. Moreover, we proved that the proposed method is globally convergent for arbitrary functions and the line search satisfies the strong Wolfe conditions. Numerical tests demonstrate the effectiveness of the ICG method when compared to certain existing methods in view of the Dolan and Mor´e performance profile. In particular, the practical application of this method in image restoration problems is explored.

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Published

2026-05-18

How to Cite

Elhamid, M. A., Moghrabi, I. A. R., Hassan, B. A., & Guler , N. (2026). An improved hybrid nonlinear conjugate gradient method and application to image restoration problems. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3366

Issue

Online First

Section

Research Articles

License

Copyright (c) 2026 Mehamdia Abd Elhamid, Issam A. R. Moghrabi, Basim A. Hassan, Nivine Guler

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