We propose a conjugate gradient method which is based on the study of the Dai-Liao conjugate gradient method. An important\r\nproperty of our proposed method is that it ensures sufficient descent independent of the accuracy of the line search. Moreover, it\r\nachieves a high-order accuracy in approximating the second-order curvature information of the objective function by utilizing the\r\nmodified secant condition proposed by Babaie-Kafaki et al. (2010). Under mild conditions, we establish that the proposed method\r\nis globally convergent for general functions provided that the line search satisfies theWolfe conditions. Numerical experiments are\r\nalso presented.
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