A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces
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2024
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Journal of Optimization Theory and Applications. Springer. ISSN 0022-3239. eISSN 1573-2878. Verfügbar unter: doi: 10.1007/s10957-024-02520-4
Zusammenfassung
The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from Gebken and Peitz (J Optim Theory Appl 188:696–723, 2021) is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the Clarke subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.
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SONNTAG, Konstantin, Bennet GEBKEN, Georg MÜLLER, Sebastian PEITZ, Stefan VOLKWEIN, 2024. A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. In: Journal of Optimization Theory and Applications. Springer. ISSN 0022-3239. eISSN 1573-2878. Verfügbar unter: doi: 10.1007/s10957-024-02520-4BibTex
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year={2024},
doi={10.1007/s10957-024-02520-4},
title={A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces},
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journal={Journal of Optimization Theory and Applications},
author={Sonntag, Konstantin and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Volkwein, Stefan}
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<dcterms:abstract>The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from Gebken and Peitz (J Optim Theory Appl 188:696–723, 2021) is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the Clarke subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.</dcterms:abstract>
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