Some three-term conjugate gradient algorithms with descent condition for unconstrained optimization models

The three-term conjugate gradient methods are among the most efficient numerical methods for solving large-scale unconstrained optimization problems. This is due to their low memory requirements in addition to their global convergence properties. Numerous research and modifications have been done recently to enhanced the performance of this method. This paper presents some modified three-term conjugate gradient methods and show that the methods satisfies the sufficient descent condition. Also, the convergence analysis for general functions was presented under the general Wolfe line search. Preliminary results of some benchmark problems have been reported to illustrates the efficiency of the proposed methods.