Memoryless quasi-newton methods based on spectral-scaling broyden family for unconstrained optimization

Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Memoryless quasi-Newton methods are studied for solving largescale 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 converges globally. Finally, some numerical experiments are given.

本文言語English
ページ(範囲)1773-1793
ページ数21
ジャーナルJournal of Industrial and Management Optimization
15
4
DOI
出版ステータスPublished - 2019
外部発表はい

ASJC Scopus subject areas

  • ビジネスおよび国際経営
  • 戦略と経営
  • 制御と最適化
  • 応用数学

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