A New Hybrid MMR-PRP Conjugate Gradient Methods With Inexact Line Search

Abstract

Conjugate gradient (CG) method is an important method for solving nonlinear unconstrained optimization problems, especially those of larg-scale. They are well-known for their global convergence properties and low memory requirement. In this paper, we propose a new hybrid conjugate gradient method for solving unconstrained optimization problems. Under the strong Wolfe-Powell (SWP) line search, the global convergence of the proposed method is established. Numerical computations reported has shown that the new hybrid CG method is better than some existing hybrid CG methods in terms of number of iterations and CPU time.