Abstract
In this paper, a new hybrid conjugate gradient algorithm is proposed for unconstrained optimization. This algorithm combines the desirable computation aspects of Polak-Ribière steps and useful theoretical features of Fletcher-Reeves CG-steps. Computational results for this algorithm are given and compared with those of the Fletcher and Polak standard CG methods showing a considerable improvement over the latter two methods.
