Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912393139847168 |
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| author | Zhang, Shixuan Zhong, Suhan |
| author_facet | Zhang, Shixuan Zhong, Suhan |
| contents | We propose moment relaxations for data-driven Wasserstein distributionally robust optimization problems. Conditions are identified to ensure asymptotic consistency of such relaxations for both single-stage and two-stage problems, together with examples that illustrate their necessity. Numerical experiments are also included to illustrate the proposed relaxations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19278 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization Zhang, Shixuan Zhong, Suhan Optimization and Control We propose moment relaxations for data-driven Wasserstein distributionally robust optimization problems. Conditions are identified to ensure asymptotic consistency of such relaxations for both single-stage and two-stage problems, together with examples that illustrate their necessity. Numerical experiments are also included to illustrate the proposed relaxations. |
| title | Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.19278 |