Abstract

In this paper, we proposed a new hybrid conjugate gradient algorithm for solving unconstrained
optimization problems as a convex combination of the Dai-Yuan algorithm, conjugate-descent
algorithm, and Hestenes-Stiefel algorithm. This new algorithm is globally convergent and satisfies the
sufficient descent condition by using the strong Wolfe conditions. The numerical results show that the
proposed nonlinear hybrid conjugate gradient algorithm is efficient and robust.

Information

Item Type:	Journal
Divisions:	» Laboratory of Computer Science and Mathematics
ePrint ID:	4827
Date Deposited:	2024-02-06
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4827

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