@Article{JCM-19-3,
author = {Zhu, De-Tong},
title = {Curvilinear Paths and Trust Region Methods with Nonmonotonic Back Tracking Technique for Unconstrained Optimization},
journal = {Journal of Computational Mathematics},
year = {2001},
volume = {19},
number = {3},
pages = {241--258},
abstract = {<p style="text-align: justify;">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. &nbsp;</p>},
issn = {1991-7139},
doi = {https://doi.org/2001-JCM-8977},
url = {https://global-sci.com/article/85470/curvilinear-paths-and-trust-region-methods-with-nonmonotonic-back-tracking-technique-for-unconstrained-optimization}
}