A modified Hestenes-Stiefel method for solving unconstrained optimization problems

Abstract

The conjugate gradient methods are among the most efficient methods for solving optimization models. This is due to its simplicity, low memory requirement and the properties of its global convergent. Many researchers try to improve this technique. In this paper, we suggested a modification of the conjugate gradient parameter with global convergence properties via exact minimization rule. Preliminary experiment was conducted using some unconstrained optimization benchmark problems. Numerical outcome showed that the new algorithm is efficient and promising as it performs better than other classical methods both in terms of number of iteration and CPU time.