Global convergence of a descent PRP type conjugate gradient method for nonconvex optimization.

Nonlinear conjugate gradient methods are often used to solve large-scale unconstrained optimization problems owing to their lower memory requirement and less computation cost. In this paper, we propose a new Polak-Ribiére-Polyak type conjugate gradient method, which satisfies the sufficient descent condition independent of any line search. A remarkable property about this method is its strong global convergence with the standard Wolfe line search conditions as well as the standard Armijo line search strategy without convexity assumption of the objective function. The numerical results demonstrating the efficiency of the proposed method are reported. [ABSTRACT FROM AUTHOR]