Work

A new family of hybrid conjugate gradient method for unconstrained optimization and its application to regression analysis

Year: 2023

Type: article

Abstract: We know many conjugate gradient algorithms (CG) for solving unconstrained optimization problems. In this paper, based on the three famous Liu–Storey (LS), Fletcher–Reeves (FR) and Polak–Ribiére–Polyak (PRP) conjugate gradient methods, a new hybrid CG method is proposed. Furthermore, the search direction satisfies the sufficient descent condition independent of the line search. Likewise, we prove, under the strong Wolfe line search, the global convergence of the new method. In this respect, numerical experiments are performed and reported, which show that the proposed method is efficient and promising. In virtue of this, the application of the proposed method for solving regression models of COVID-19 is provided.

Source: RAIRO - Operations Research

DOI: 10.1051/ro/2023196

Authors: Sabrina Ben Hanachi, Badreddine Sellami, Mohammed Belloufi

Institution: Mohamed-Cherif Messaadia University

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid:

OpenAlex ID: W4390044153