An improved Wei-Yao-Liu nonlinear conjugate gradient method for optimization computation

In this paper, we take a little modification to the Wei-Yao-Liu nonlinear conjugate gradient method proposed by Wei et al. [Z. Wei, S. Yao, L. Liu, The convergence properties of some new conjugate gradient methods, Appl. Math. Comput. 183 (2006) 1341-1350] such that the modified method possesses better convergence properties. In fact, we prove that the modified method satisfies sufficient descent condition with greater parameter σ∈0,12 in the strong Wolfe line search and converges globally for nonconvex minimization. We also extend these results to the Hestenes-Stiefel method and prove that the modified HS method is globally convergent for nonconvex functions with the standard Wolfe conditions. Numerical results are reported by using some test problems in the CUTE library.