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A Retrospective Trust Region Algorithm with Trust Region Converging to Zero

A Retrospective Trust Region Algorithm with Trust Region Converging to Zero
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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.

Author Details

Jinyan Fan

Jianyu Pan

Hongyan Song

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