Year: 2022
Author: Xiaoyu Wang, Yaxiang Yuan
Journal of Computational Mathematics, Vol. 40 (2022), Iss. 2 : pp. 294–334
Abstract
We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic models. Especially, we propose a specific algorithm termed STRME, in which the trust-region radius depends linearly on the gradient used to define the latest model. The complexity results of the STRME method in nonconvex, convex and strongly convex settings are presented, which match those of the existing algorithms based on probabilistic properties. In addition, several numerical experiments are carried out to reveal the benefits of the proposed methods compared to the existing stochastic trust-region methods and other relevant stochastic gradient methods.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2012-m2020-0144
Journal of Computational Mathematics, Vol. 40 (2022), Iss. 2 : pp. 294–334
Published online: 2022-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 41
Keywords: Trust-region methods Stochastic optimization Probabilistic models Trust-region radius Global convergence.