CSpace
Almost sure convergence of randomised-difference descent algorithm for stochastic convex optimisation
Geng, Xiaoxue1,2; Huang, Gao3; Zhao, Wenxiao1,2
2021-08-15
Source PublicationIET CONTROL THEORY AND APPLICATIONS
ISSN1751-8644
Pages12
AbstractStochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep neural networks, it essentially relies on obtaining the unbiased estimates of gradients/subgradients of the objective functions. In this paper, by constructing the randomised differences of the objective function, a gradient-free algorithm, named the stochastic randomised-difference descent algorithm, is proposed for stochastic convex optimisation. Under the strongly convex assumption of the objective function, it is proved that the estimates generated from stochastic randomised-difference descent converge to the optimal value with probability one, and the convergence rates of both the mean square error of estimates and the regret functions are established. Finally, some numerical examples are prsented.
DOI10.1049/cth2.12184
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018YFA0703800] ; National Nature Science Foundation of China[62022048] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27000000]
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000684957300001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59105
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhao, Wenxiao
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
3.Tsinghua Univ, Dept Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Geng, Xiaoxue,Huang, Gao,Zhao, Wenxiao. Almost sure convergence of randomised-difference descent algorithm for stochastic convex optimisation[J]. IET CONTROL THEORY AND APPLICATIONS,2021:12.
APA Geng, Xiaoxue,Huang, Gao,&Zhao, Wenxiao.(2021).Almost sure convergence of randomised-difference descent algorithm for stochastic convex optimisation.IET CONTROL THEORY AND APPLICATIONS,12.
MLA Geng, Xiaoxue,et al."Almost sure convergence of randomised-difference descent algorithm for stochastic convex optimisation".IET CONTROL THEORY AND APPLICATIONS (2021):12.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Geng, Xiaoxue]'s Articles
[Huang, Gao]'s Articles
[Zhao, Wenxiao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Geng, Xiaoxue]'s Articles
[Huang, Gao]'s Articles
[Zhao, Wenxiao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Geng, Xiaoxue]'s Articles
[Huang, Gao]'s Articles
[Zhao, Wenxiao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.