STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS
Wang, Xiaoyu1,2; Yuan, Ya-xiang3
刊名JOURNAL OF COMPUTATIONAL MATHEMATICS
2021-09-16
页码41
关键词Trust-region methods Stochastic optimization Probabilistic models Trust region radius Global convergence
ISSN号0254-9409
DOI10.4208/jcm.2012-m2020-0144
英文摘要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.
资助项目National Natural Science Foundation of China[11331012] ; National Natural Science Foundation of China[11688101]
WOS研究方向Mathematics
语种英语
出版者GLOBAL SCIENCE PRESS
WOS记录号WOS:000712898300001
内容类型期刊论文
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59459]  
专题中国科学院数学与系统科学研究院
通讯作者Wang, Xiaoyu
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Comp Math & Sci Engn Comp, Beijing 100190, Peoples R China
3.Chinese Acad Sci, State Key Lab Sci Engn Comp, Inst Comp Math & Scient Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Wang, Xiaoyu,Yuan, Ya-xiang. STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS[J]. JOURNAL OF COMPUTATIONAL MATHEMATICS,2021:41.
APA Wang, Xiaoyu,&Yuan, Ya-xiang.(2021).STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS.JOURNAL OF COMPUTATIONAL MATHEMATICS,41.
MLA Wang, Xiaoyu,et al."STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS".JOURNAL OF COMPUTATIONAL MATHEMATICS (2021):41.
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