An active-set memoryless quasi-Newton method based on a spectral-scaling Broyden family for bound constrained optimization

Shummin Nakayama, Yasushi Narushima, Hiroaki Nishio, Hiroshi Yabe

研究成果: Article査読

抄録

In this paper, we consider an active-set algorithm for solving large-scale bound constrained optimization problems. First, by incorporating a restart technique, we modify the active-set strategy by Yuan and Lu (2011) and combine it with the memoryless quasi-Newton method based on a modified spectral-scaling Broyden family. Then, we propose an algorithm of our method with the framework of the Armijo line search, and show its global convergence. Finally, we illustrate some numerical experiments to investigate how the parameter choice in our method affects numerical performance.

本文言語English
論文番号100012
ジャーナルResults in Control and Optimization
3
DOI
出版ステータスPublished - 2021 6

ASJC Scopus subject areas

  • 制御と最適化
  • 応用数学
  • 人工知能
  • モデリングとシミュレーション
  • 制御およびシステム工学

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