Global Convergence Analysis of a new Hybrid Conjugate Gradient Method for Unconstraint Optimization Problems

Abstract In this study, we introduce a novel hybrid conjugate gradient [CG] to solve an efficient and effective unconstrained optimization problem. The parameter θ k is convex combination of the method β k B A 2 }}_{\bf{k}}^{{\bf{FR}}}$?> F R . Under strong Wolfe line search conditions (SWC), have shown that globally convergent, proposed CG able generate descending direction at each iteration. numerical results presented, in paper demonstrate strategy both promising.