A nonmonotone memory gradient method for unconstrained optimization

Memory gradient methods are used for unconstrained optimization, especially large scale problems. They were first proposed by Miele & Cantrell and Cragg & Levy. Recently Narushima & Yabe proposed a new memory gradient method which generates a descent search direction for the objective function at every iteration and converges globally to the solution if the Wolfe conditions are satisfied within the line search strategy. In this paper, we propose a nonmonotone memory gradient method based on this work. We show that our method converges globally to the solution. Our numerical results show that the proposed method is efficient for some standard test problems if we choose a parameter included in the method suitably.