Two modified nonlinear conjugate gradient methods with disturbance factors for unconstrained optimization

The nonlinear conjugate gradient method (CGM) is a very effective iterative method for solving large-scale optimal problems. In this paper, based on a variant of Polak–Ribière–Polyak method, two modified CGMs with disturbance factors are proposed. By the disturbance factors, the two proposed methods not only generate sufficient descent direction at each iteration but also converge globally for nonconvex minimization if the strong Wolfe line search is used. Finally, elementary numerical experiment results are reported, which show that the proposed methods are promising.