Abstract

The conjugate gradient (CG) method has played a special role in solving large-
scale nonlinear optimization due to the simplicity of their iterations and their very low
memory requirements. In this paper, a new nonlinear conjugate gradient method was pro-
posed for large-scale unconstrained optimization. It is important that the proposed method
produce sucient descent search direction at every iteration with the strong Wolfe line
searches, and the global convergence for general non-convex functions can be guaranteed.
The numerical results show that one of our new CG methods is very encouraging.

Information

Item Type:	Conference
Divisions:	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID:	427
Date Deposited:	2015-10-14
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/427

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