New Conjugate Gradient Algorithm for Solving Unconstrained Optimization Problems

Abstract

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 global 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.