Abstract

Abstract Conjugate gradient methods are an important class of methods for unconstrained
optimization, especially for large-scale problems. Recently, they have been much studied. In
this paper, a newfamily of conjugate gradientmethod is proposed for unconstrained optimiza-
tion. This method includes the already existing two practical nonlinear conjugate gradient
methods, which produces a descent search direction at every iteration and converges globally
provided that the line search satis?es the Wolfe conditions. The numerical experiments are
done to test the ef?ciency of the new method, which implies the new method is promising.
In addition the methods related to this family are uniformly discussed.

Information

Item Type:	Journal
Divisions:	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID:	517
Date Deposited:	2016-04-01
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/517

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