A Non-monotone Memory Gradient Method for Unconstrained Optimization

The memory gradient method is used for unconstrained optimization, especially large scale problems. In this paper, we develop a nonmonotone memory gradient method for unconstrained optimization, where a class of memory gradient direction is combined efficiently. The global and Rlinear convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.