A generalized conditional gradient method for multiobjective composite optimization problems

This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functions. To tackle these problems, we propose a generalized version of the conditional g...

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Veröffentlicht in:Optimization Jg. 74; H. 2; S. 473 - 503
Hauptverfasser: Assunção, P. B., Ferreira, O. P., Prudente, L. F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 25.01.2025
Taylor & Francis LLC
Schlagworte:
Asymptotic methods
Conditional gradient method
constrained optimization problem
Convexity
Frank-Wolfe method
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimality
ISSN:0233-1934, 1029-4945
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Zusammenfassung:This article deals with multiobjective composite optimization problems that consist of simultaneously minimizing several objective functions, each of which is composed of a combination of smooth and non-smooth functions. To tackle these problems, we propose a generalized version of the conditional gradient method, also known as Frank-Wolfe method. The method is analysed with three step size strategies, including Armijo-type, adaptive, and diminishing step sizes. We establish asymptotic convergence properties and iteration-complexity bounds, with and without convexity assumptions on the objective functions. Numerical experiments illustrating the practical behaviour of the methods are presented.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2023.2257709