An acceleration of gradient descent algorithm with backtracking for unconstrained optimization

In this paper we introduce an acceleration of gradient descent algorithm with backtracking. The idea is to modify the steplength t(k) by means of a positive parameter theta(k) , in a multiplicative manner, in such a way to improve the behaviour of the classical gradient algorithm. It is shown that the resulting algorithm remains linear convergent, but the reduction in function value is significantly improved.