A scalar modification of three-term PRP-DL conjugate gradient method for solving large-scaled unconstrained optimization problems

Unconstrained optimization problems arise in numerous fields. This study presents the introduction of a hybrid Polak-Ribi‘ere-Polyak(PRP)-Dai-Liao(DL) conjugate gradient(CG) method with a modified scalar for the purpose of solving large-scaled unconstrained optimiza tion problems. The proposed method involves the modification of the scalar in the PRP-DL conjugate gradient method in order to improve the performance of the algorithm, specifically when addressing large-scale problems. The convergence analysis of the proposed method is established and proved under the strong Wolfe-Powell line search. Numerical results on various test functions show that the proposed method is more efficient and robust than several existing CG methods. Overall, the proposed method is a new promising CG method for solving unconstrained optimization problems

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