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  • 标题:A fast quasi-Newton-type method for large-scale stochastic optimisation ⁎
  • 本地全文:下载
  • 作者:Adrian Wills ; Thomas B. Schön ; Carl Jidling
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:1249-1254
  • DOI:10.1016/j.ifacol.2020.12.1849
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second-order information. In this work we propose a direct least-squares approach conceptually similar to the limited memory quasi-Newton methods, but that computes the search direction in a slightly different way. This is achieved in a fast and numerically robust manner by maintaining a Cholesky factor of low dimension. The performance is demonstrated on real-world benchmark problems which shows improved results in comparison with already established methods.
  • 关键词:KeywordsOptimisation problemsLarge-scale problemsStochastic systemsCholesky factorisationNeural networks
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