Abstract

Conjugate gradient methods (CG) are an important class of methods for solving unconstrained
optimization problems, especially for large-scale problems. Recently, they have been much
studied. In this paper, we propose a new conjugate gradient method for unconstrained optimization.
This method is a convex combination of Fletcher and Reeves (abbreviated FR), Polak–Ribiere–Polyak
(abbreviated PRP) and Dai and Yuan (abbreviated DY) methods. The new conjugate gradient 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 this method. The numerical experiments are done
to test the efficiency of the proposed method, which confirms its promising potentials.

Information

Item Type:	Journal
Divisions:	» Laboratory of Computer Science and Mathematics
ePrint ID:	4826
Date Deposited:	2024-02-06
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4826

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