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

Fuente: arXiv
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Auteurs principaux: Brilli, Andrea, Kimiaei, Morteza, Liuzzi, Giampaolo, Lucidi, Stefano
Format: Preprint
Publié: 2023
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author Brilli, Andrea
Kimiaei, Morteza
Liuzzi, Giampaolo
Lucidi, Stefano
author_facet Brilli, Andrea
Kimiaei, Morteza
Liuzzi, Giampaolo
Lucidi, Stefano
contents 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 $ε$ is ${\cal O}(ε^{-2})$ in the worst case.
format Preprint
id arxiv_https___arxiv_org_abs_2302_05274
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Worst case complexity bounds for linesearch-type derivative-free algorithms
Brilli, Andrea
Kimiaei, Morteza
Liuzzi, Giampaolo
Lucidi, Stefano
Optimization and Control
90C56
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 $ε$ is ${\cal O}(ε^{-2})$ in the worst case.
title Worst case complexity bounds for linesearch-type derivative-free algorithms
topic Optimization and Control
90C56
url https://arxiv.org/abs/2302.05274