Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866914654929813504 |
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| author | Baak, Werner Goerigk, Marc Kasperski, Adam Zieliński, Paweł |
| author_facet | Baak, Werner Goerigk, Marc Kasperski, Adam Zieliński, Paweł |
| contents | In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel variant of ordered weighted averaging (OWA) for optimization problems. It generalizes the classic OWA approach, which includes robust min-max optimization as a special case, as well as min-max regret optimization. We derive new complexity results for this setting, including insights into the inapproximability and approximability of this problem. In particular, we provide stronger positive approximation results that asymptotically improve the previously best-known bounds for the classic OWA approach. In computational experiments, we evaluate the quality of the proposed methods and compare the proposed setting with classic OWA and min-max regret approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08522 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging Baak, Werner Goerigk, Marc Kasperski, Adam Zieliński, Paweł Optimization and Control In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel variant of ordered weighted averaging (OWA) for optimization problems. It generalizes the classic OWA approach, which includes robust min-max optimization as a special case, as well as min-max regret optimization. We derive new complexity results for this setting, including insights into the inapproximability and approximability of this problem. In particular, we provide stronger positive approximation results that asymptotically improve the previously best-known bounds for the classic OWA approach. In computational experiments, we evaluate the quality of the proposed methods and compare the proposed setting with classic OWA and min-max regret approaches. |
| title | Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2308.08522 |