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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16120 |
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| _version_ | 1866913108467908608 |
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| author | Li, Zhehao Wu, Yanchen Chen, Jian Mao, Xiaojie |
| author_facet | Li, Zhehao Wu, Yanchen Chen, Jian Mao, Xiaojie |
| contents | Data-driven inverse optimization seeks to estimate unknown parameters in an optimization model from observations of optimization solutions. Many existing methods are ineffective in handling noisy and suboptimal solution observations and also suffer from computational challenges. In this paper, we build a connection between inverse optimization and the Fenchel-Young (FY) loss originally designed for structured prediction, proposing a FY loss approach to data-driven inverse optimization. This new approach is amenable to efficient gradient-based optimization, hence much more efficient than existing methods. We provide theoretical guarantees for the proposed method and use extensive simulation and real-data experiments to demonstrate its significant advantage in parameter estimation accuracy, decision error and computational speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization Li, Zhehao Wu, Yanchen Chen, Jian Mao, Xiaojie Optimization and Control Machine Learning Data-driven inverse optimization seeks to estimate unknown parameters in an optimization model from observations of optimization solutions. Many existing methods are ineffective in handling noisy and suboptimal solution observations and also suffer from computational challenges. In this paper, we build a connection between inverse optimization and the Fenchel-Young (FY) loss originally designed for structured prediction, proposing a FY loss approach to data-driven inverse optimization. This new approach is amenable to efficient gradient-based optimization, hence much more efficient than existing methods. We provide theoretical guarantees for the proposed method and use extensive simulation and real-data experiments to demonstrate its significant advantage in parameter estimation accuracy, decision error and computational speed. |
| title | A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2502.16120 |