Academic Editor: Joaquim J. Júdice
Copyright © 2009 Jianguo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Two nonlinear conjugate gradient-type methods for solving unconstrained optimization problems are proposed. An attractive property of the methods, is that, without any line search,
the generated directions always descend. Under some mild conditions, global convergence results for
both methods are established. Preliminary numerical results show that these proposed methods are
promising, and competitive with the well-known PRP method.