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, we propose a new two-parameter family of conjugate
gradient methods for unconstrained optimization. The two-parameter family of
methods not only includes the already existing three practical nonlinear con-
jugate gradient methods, but has other family of conjugate gradient methods
as subfamily. The two-parameter family of methods with the Wolfe line search
is shown to ensure the descent property of each search direction. Some general
convergence results are also established for the two-parameter family of methods.
The numerical results show that this method is efficient for the given test problems.
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:	513
Date Deposited:	2016-03-23
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/513

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