A trust-region-based BFGS method with line search technique for symmetric nonlinear equations.
Yuan, Gonglin, Meng, Shide, Wei, Zengxin (2009)
Advances in Operations Research
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Displaying similar documents to “Convergence analysis of adaptive trust region methods”
A trust-region-based BFGS method with line search technique for symmetric nonlinear equations.
A truncated descent HS conjugate gradient method and its global convergence.
Cheng, Wanyou, Zhang, Zongguo (2009)
Mathematical Problems in Engineering
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An accurate active set Newton algorithm for large scale bound constrained optimization
Li Sun, Guoping He, Yongli Wang, Changyin Zhou (2011)
Applications of Mathematics
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A new algorithm for solving large scale bound constrained minimization problems is proposed. The algorithm is based on an accurate identification technique of the active set proposed by Facchinei, Fischer and Kanzow in 1998. A further division of the active set yields the global convergence of the new algorithm. In particular, the convergence rate is superlinear without requiring the strict complementarity assumption. Numerical tests demonstrate the efficiency and performance of the...
Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization.
Zhang, Jianguo, Xiao, Yunhai, Wei, Zengxin (2009)
Mathematical Problems in Engineering
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Newton-Krylov type algorithm for solving nonlinear least squares problems.
Abdel-Aziz, Mohammedi R., El-Alem, Mahmoud M. (2009)
International Journal of Mathematics and Mathematical Sciences
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Two-phase generalized reduced gradient method for constrained global optimization.
El Mouatasim, Abdelkrim (2010)
Journal of Applied Mathematics
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Random perturbation of the variable metric method for unconstrained nonsmooth nonconvex optimization
Abdelkrim El Mouatasim, Rachid Ellaia, José Souza de Cursi (2006)
International Journal of Applied Mathematics and Computer Science
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We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations is introduced. An algorithm is proposed and numerical results are presented, showing that the method is computationally effective...
A new method for nonsmooth convex optimization.
Wei, Z., Qi, L., Birge, J.R. (1998)
Journal of Inequalities and Applications [electronic only]
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On a Second-Order Step-Size Algorithm
Nada I. Djuranović-Miličić (2002)
The Yugoslav Journal of Operations Research
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A simple sufficient descent method for unconstrained optimization.
Zhang, Ming-Liang, Xiao, Yun-Hai, Zhou, Dangzhen (2010)
Mathematical Problems in Engineering
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Global convergence property of modified Levenberg-Marquardt methods for nonsmooth equations
Shou-qiang Du, Yan Gao (2011)
Applications of Mathematics
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In this paper, we discuss the globalization of some kind of modified Levenberg-Marquardt methods for nonsmooth equations and their applications to nonlinear complementarity problems. In these modified Levenberg-Marquardt methods, only an approximate solution of a linear system at each iteration is required. Under some mild assumptions, the global convergence is shown. Finally, numerical results show that the present methods are promising.