Year: 2016
Author: Jinyan Fan, Jianyu Pan, Hongyan Song
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 4 : pp. 421–436
Abstract
We propose a retrospective trust region algorithm with the trust region converging to zero for the unconstrained optimization problem. Unlike traditional trust region algorithms, the algorithm updates the trust region radius according to the retrospective ratio, which uses the most recent model information. We show that the algorithm preserves the global convergence of traditional trust region algorithms. The superlinear convergence is also proved under some suitable conditions.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1601-m2015-0399
Journal of Computational Mathematics, Vol. 34 (2016), Iss. 4 : pp. 421–436
Published online: 2016-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 16
Keywords: Retrospective trust region algorithm Unconstrained optimization Superlinear convergence.