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Conjugate gradient methods are probably the most famous iterative methods for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. It is well known that the search direction plays a main role in the line search method. In this article, we propose a new search direction with the Wolfe line search technique for solving unconstrained optimization problems. Under the above line searches and some assumptions, the global convergence properties of the given methods are discussed. Numerical results and comparisons with other CG methods are given.

Item Type	Journal
Divisions	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID	191
Date Deposited	2014-12-11
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/191

@article{uniusa191,
    title={Descent property and global convergence of a new search direction method for unconstrained optimizationMethod for Unconstrained Optimization},
    author={Mohammed BELLOUFI and Rachid Benzine},
    journal={Numerical Functional Analysis and Optimization}
    year={2014},
    volume={36},
    number={2},
    pages={169-180},
    publisher={Taylor & Francis Group}
}