A new scaled steepest descent method for unconstrained optimization with global convergence properties

The steepest descent method is the simplest gradient method for solving unconstrained optimization problems. In this study, a new scaled search direction of steepest descent method is proposed. The proposed method is motivated by Andrei's approach of scaled conjugate gradient method. The numeri...

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书目详细资料
发表在:Journal of Engineering and Applied Sciences
主要作者: 2-s2.0-85052916904
格式: 文件
语言:English
出版: Medwell Journals 2018
在线阅读:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052916904&doi=10.3923%2fjeasci.2018.5442.5445&partnerID=40&md5=822e79dfab1cbbc4ca1e6247aba3db69
实物特征
总结:The steepest descent method is the simplest gradient method for solving unconstrained optimization problems. In this study, a new scaled search direction of steepest descent method is proposed. The proposed method is motivated by Andrei's approach of scaled conjugate gradient method. The numerical results show that the proposed method outperforms than the other classical steepest descent method. © Medwell Journals, 2018.
ISSN:1816949X
DOI:10.3923/jeasci.2018.5442.5445