(untitled) (Statistics, Optimization & Information Computing, 2025)
We are interested in the performance of nonlinear conjugate gradient methods for unconstrained optimization. In particular, we address the conjugate gradient algorithm with strong Wolfe inexact line search. Firstly, we study the descent property of the search direction of the considered conjugate gradient algorithm based on a new direction obtained from a new parameter. The main objective of this parameter is to improve the speed of the convergence of the obtained algorithm. Then, we present a complete study that shows the global convergence of this algorithm. Finally, we establish comparative numerical experiments on well-known test examples to show the efficiency and robustness of our algorithm compared to other recent algorithms.