A DL -like Hybrid of DY Conjugate Gradient Algorithm for Unconstrained Optimization Problems with Application in Mode Function

Abstract

Conjugate gradient methods have come a long way in solving system of equations and large scaled optimization problems. In this paper, a hybrid conjugate gradient method which incorporates the DY update parameter and herein constructed conjugate gradient solver that uses Dai-Liao conjugacy conditions is proposed for nonlinear unconstrained optimization problems. It is shown that this proposed method possesses descent search direction at every iteration and thereby converges globally under standard Wolfe line search conditions. Proofs of its robustness and efficiency were demonstrated through numerical tests and application to mode function when compared with other known optimization conjugate gradient algorithms.