A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization

Fuente: arXiv
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Autor principal: Mansueto, Pierluigi
Formato: Preprint
Publicado: 2025
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author Mansueto, Pierluigi
author_facet Mansueto, Pierluigi
contents We introduce a nonmonotone extension of the Front Descent framework for multiobjective optimization. The method uses novel nonmonotone line searches that allow temporary increases in some objective functions. To our knowledge, this is the first descent algorithm employing nonmonotone strategies to generate point sets approximating the Pareto front. We establish convergence properties for the resulting sequences of sets, analogous to the original framework, and present numerical results confirming the approach's consistency in the bound-constrained setting.
format Preprint
id arxiv_https___arxiv_org_abs_2509_02409
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
Mansueto, Pierluigi
Optimization and Control
90C29, 90C30
We introduce a nonmonotone extension of the Front Descent framework for multiobjective optimization. The method uses novel nonmonotone line searches that allow temporary increases in some objective functions. To our knowledge, this is the first descent algorithm employing nonmonotone strategies to generate point sets approximating the Pareto front. We establish convergence properties for the resulting sequences of sets, analogous to the original framework, and present numerical results confirming the approach's consistency in the bound-constrained setting.
title A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
topic Optimization and Control
90C29, 90C30
url https://arxiv.org/abs/2509.02409