Nonlinear conjugate gradient (CG) method holds an important role in solving large-scale unconstrained optimization problems. In this paper, we suggest a new modification of CG coefficient �� that satisfies sufficient descent condition and possesses global convergence property under strong Wolfe line search. The numerical results show that our new method is more efficient compared with other CG formulas tested.
Conjugate gradient algorithms are important for solving unconstrained optimization
problems, so that we present in this paper conjugate gradient algorithm depending on
improving conjugate coefficient achieving sufficient descent condition and globa
l
convergence by doing hybrid between the two conjugate coefficients [1] and
[2]. Numerical results show the efficiency of the suggested algorithm after its
application on several standard problems and comparing it with other conjugate gradient
algorithms according to number of iterations, function value and norm of gradient vector.
