A Modified Hestenes-Stiefel Conjugate Gradient Method and its Global convergence for unconstrained optimization

Abstract

In this paper, we proposed a modified Hestenes-Stiefel (HS) conjugate
gradient method. This achieves a high order accuracy in approximating the second
order curvature information of the objective function by utilizing the modified
secant condition which is proposed by Babaie-Kafaki [1], also we derive a nonquadratic
conjugate gradient model. The important property of the suggestion
method that is satisfy the descent property and global convergence independent of
the accuracy of the line search. In addition, we prove the global convergence under
some suitable conditions, and we reported the numerical results under these
conditions.