A hybrid conjugate gradient method with descent property for unconstrained optimization

Abstract In this paper, based on some famous previous conjugate gradient methods, a new hybrid conjugate gradient method was presented for unconstrained optimization. The proposed method can generate decent directions at every iteration, moreover, this property is independent of the steplength line search. Under the Wolfe line search, the proposed method possesses global convergence. Medium-scale numerical experiments and their performance profiles are reported, which show that the proposed method is promising.

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