An efficient hybrid conjugate gradient method for unconstrained optimization and image restoration problems

Articles in Press

Document Type : Research Article

Authors

• Choubeila Souli ¹
• Raouf Ziadi ¹
• Imad Eddine Lakhdari ²
• Assma Leulmi ¹

¹ Laboratory of Fundamental and Numerical Mathematics (LMFN), University Ferhat Abbas Setif 1, Algeria.

² Laboratory of Mathematical Analysis, Probability and Optimizations, Biskra University, Algeria.

10.22067/ijnao.2024.88087.1449

Abstract

The conjugate gradient (CG) method is an optimization technique known for its rapid convergence; it has blossomed into significant developments and applications. Numerous variations of CG methods have emerged to enhance computational efficiency and address real-world challenges. In this work, a novel conjugate gradient method is introduced to solve nonlinear unconstrained optimization problems. Based on the combination of PRP (Polak-Ribière-Polyak), HRM (Hamoda-Rivaie-Mamat) and NMFR (New modified Fletcher-Reeves) algorithms, our method produces a descent direction without depending on any line search. Moreover, it enjoys global convergence under mild assumptions and is applied successfully on various standard test problems as well as image processing. The numerical results indicate that the proposed method outperforms several existing methods in terms of efficiency.

Keywords

• Unconstrained optimization
• Hybrid conjugate gradient
• Global convergence
• Image restoration

Main Subjects

• Optimization

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