Year: 2002
Author: Ding-Guo Pu
Journal of Computational Mathematics, Vol. 20 (2002), Iss. 3 : pp. 289–300
Abstract
In this paper, motivated by the Martinez and Qi methods [1], we propose one type of globally convergent inexact generalized Newton methods to solve unconstrained optimization problems in which the objective functions are not twice differentiable, but have LC gradient. They make the norm of the gradient decreasing. These methods are implementable and globally convergent. We prove that the algorithms have superlinear convergence rates under some mild conditions.
The methods may also be used to solve nonsmooth equations.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2002-JCM-8918
Journal of Computational Mathematics, Vol. 20 (2002), Iss. 3 : pp. 289–300
Published online: 2002-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Nonsmooth optimization Inexact Newton method Generalized Newton method Global convergence superlinear rate.