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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number100012
JournalResults in Control and Optimization
Volume3
DOIs
Publication statusPublished - 2021 Jun

Keywords

  • Active-set method
  • Bound constrained optimization
  • Broyden family
  • Global convergence
  • Memoryless quasi-Newton method

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics
  • Artificial Intelligence
  • Modelling and Simulation
  • Control and Systems Engineering

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