Worst case complexity bounds for linesearch-type derivative-free algorithms

Andrea Brilli, Morteza Kimiaei, Giampaolo Liuzzi, Stefano Lucidi

Veröffentlichungen: Working PaperPreprint

Abstract

This paper is devoted to the analysis of worst case complexity bounds for linesearch-type derivative-free algorithms for the minimization of general non-convex smooth functions. We prove that two linesearch-type algorithms enjoy the same complexity properties which have been proved for pattern and direct search algorithms. In particular, we consider two derivative-free algorithms based on two different linesearch techniques and manage to prove that the number of iterations and of function evaluations required to drive the norm of the gradient of the objective function below a given threshold $\epsilon$ is ${\cal O}(\epsilon^{-2})$ in the worst case.
OriginalspracheEnglisch
PublikationsstatusVeröffentlicht - 10 Feb. 2023

ÖFOS 2012

  • 101016 Optimierung
  • 101014 Numerische Mathematik

Zitationsweisen