A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913229570048000 |
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| author | Sonntag, Konstantin Gebken, Bennet Müller, Georg Peitz, Sebastian Volkwein, Stefan |
| author_facet | Sonntag, Konstantin Gebken, Bennet Müller, Georg Peitz, Sebastian Volkwein, Stefan |
| contents | The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06376 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces Sonntag, Konstantin Gebken, Bennet Müller, Georg Peitz, Sebastian Volkwein, Stefan Optimization and Control 90C29, 49J52, 35B37, 34K35 The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] 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 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. |
| title | A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces |
| topic | Optimization and Control 90C29, 49J52, 35B37, 34K35 |
| url | https://arxiv.org/abs/2402.06376 |