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The nonlinear conjugate gradient method (CGM) is a very effective iterative method for solving large-scale optimal problems. In this paper, based on a variant of Polak–Ribière–Polyak method, two modified CGMs with disturbance factors are proposed. By the disturbance factors, the two proposed methods not only generate sufficient descent direction at each iteration but also converge globally for nonconvex...
The nonlinear conjugate gradient method (CGM) is a very effective way in solving large-scale optimal problems. In this paper, a modification to the Dai–Yuan (DY) nonlinear CGM is discussed, and then a sufficient descent CGM for unconstrained optimization is proposed. Unlike the DY CGM, at each iteration, the presented CGM always generates a sufficient descent direction depending on no line search...
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