An accelerated proximal gradient method for multiobjective optimization

This paper presents an accelerated proximal gradient method for multiobjective optimization, in which each objective function is the sum of a continuously differentiable, convex function and a closed, proper, convex function. Extending first-order methods for multiobjective problems without scalariz...

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Veröffentlicht in:	Computational optimization and applications Jg. 86; H. 2; S. 421 - 455
Hauptverfasser:	Tanabe, Hiroki, Fukuda, Ellen H., Yamashita, Nobuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2023 Springer Nature B.V
Schlagworte:	Algorithms Convergence Convex and Discrete Geometry Iterative methods Management Science Mathematics Mathematics and Statistics Methods Multiple objective analysis Operations Research Operations Research/Decision Theory Optimization Statistics FISTA Global rate of convergence 90C25 Multiobjective optimization Pareto optimality 90C29 First-order method Proximal gradient method
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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