The convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models

The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL∗) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising.