Memory gradient method with Goldstein line search

In this paper, we present a multi-step memory gradient method with Goldstein line search for unconstrained optimization problems and prove its global convergence under some mild conditions. We also prove the linear convergence rate of the new method when the objective function is uniformly convex. Numerical results show that the new algorithm is suitable to solve large-scale optimization problems and is more stable than other similar methods in practical computation.