A Hybrid Conjugate Gradient Method for unconstrained optimization

In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization. As a remarkable feature, the search directions of the proposed method satisfy the sufficient descent condition, independently of line searches. Under some standard assumptions, the global convergence of the proposed method is proved for general functions. Numerical comparisons on some test problems indicate the efficiency and robustness of the proposed method in practice.