A new hybrid conjugate gradient algorithm for unconstrained optimization

It is well known that conjugate gradient methods are useful for solving large-scale unconstrained nonlinear optimization problems. In this paper, we consider combining the best features of two methods. particular, give a new method, based on hybridization DY (Dai-Yuan), and HZ (Hager-Zhang) The hybrid parameters chosen such proposed method satisfies conjugacy sufficient descent conditions. shown maintains global convergence property above numerical results described set standard test performance better than in most cases.