Memoryless quasi-Newton methods based on spectral-scaling Broyden family for unconstrained optimization

Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe

研究成果: Article

1 引用 (Scopus)

抜粋

Memoryless quasi-Newton methods are studied for solving large- scale unconstrained optimization problems. Recently, memoryless quasi-Newton methods based on several kinds of updating formulas were proposed. Since the methods closely related to the conjugate gradient method, the methods are promising. In this paper, we propose a memoryless quasi-Newton method based on the Broyden family with the spectral-scaling secant condition. We focus on the convex and preconvex classes of the Broyden family, and we show that the proposed method satisfies the sufficient descent condition and con- verges globally. Finally, some numerical experiments are given.

元の言語English
ページ(範囲)1-21
ページ数21
ジャーナルJournal of Industrial and Management Optimization
13
発行部数5
DOI
出版物ステータスPublished - 2017 1 1

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

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