A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property

. Conjugate gradient methods are widely used for unconstrained optimization, especially large scale problems. However, the strong Wolfe conditions are usually used in the analyses and implementations of conjugate gradient methods. This paper presents a new version of the conjugate gradient method, which converges globally provided the line search satisfies the standard Wolfe conditions. The conditions on the objective function are also weak, which are similar to that required by the Zoutendijk condition. Key words. unconstrained optimization, new conjugate gradient method, Wolfe conditions, global convergence. AMS subject classifications. 65k, 90c 1. Introduction. Our problem is to minimize a function of n variables min f(x); (1.1) where f is smooth and its gradient g(x) is available. Conjugate gradient methods for solving (1.1) are iterative methods of the form x k+1 = x k + ff k d k ; (1.2) where ff k ? 0 is a steplength, d k is a search direction. Normally the search direction at..