A Globally Convergence Spectral Conjugate Gradient Method for Solving Unconstrained Optimization Problems

In this paper, a modified spectral conjugate gradient method for solving unconstrained optimization problems is studied, which has sufficient descent direction and global convergence with an inexact line searches. The Fletcher-Reeves restarting criterion was employed to the standard and new versions and gave dramatic savings in the computational time. The Numerical results show that the proposed method is effective by comparing it with the FR-method.