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In this paper, we propose a new class of adaptive trust region methods for unconstrained optimization problems and develop some convergence properties. In the new algorithms, we use the current iterative information to define a suitable initial trust region radius at each iteration. The initial trust region radius is more reasonable in the sense that the trust region model and the objective function are more consistent at the current iterate. The global convergence, superlinear and quadraticdoi:10.1051/ro:2007007 fatcat:jmbcx3wjqveefijfd2uczlxx2m