Curvilinear Paths and Trust Region Methods with Nonmonotonic Back Tracking Technique for Unconstrained Optimization
Year: 2001
Author: De-Tong Zhu
Journal of Computational Mathematics, Vol. 19 (2001), Iss. 3 : pp. 241–258
Abstract
In this paper we modify type approximate trust region methods via two curvilinear paths for unconstrained optimization. A mixed strategy using both trust region and line search techniques is adopted which switches to back tracking steps when a trial step produced by the trust region subproblem is unacceptable. We give a series of properties of both optimal path and modified gradient path. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. A nonmonotonic criterion is used to speed up the convergence progress in some ill-conditioned cases.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2001-JCM-8977
Journal of Computational Mathematics, Vol. 19 (2001), Iss. 3 : pp. 241–258
Published online: 2001-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Curvilinear paths Trust region methods Nonmonotonic technique Unconstrained optimization.