Optimization with Trained Machine Learning Models Embedded
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916090286702592 |
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| author | Schweidtmann, Artur M. Bongartz, Dominik Mitsos, Alexander |
| author_facet | Schweidtmann, Artur M. Bongartz, Dominik Mitsos, Alexander |
| contents | Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models frequently lead to large problems, they also exhibit homogeneous structures and repeating patterns (e.g., layers in ANNs). Thus, specialized solution strategies can be used for large problem classes. Recently, there have been some promising works proposing specialized reformulations using mixed-integer programming or reduced space formulations. However, further work is needed to develop more efficient solution approaches and keep up with the rapid development of new ML model architectures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2207_12722 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Optimization with Trained Machine Learning Models Embedded Schweidtmann, Artur M. Bongartz, Dominik Mitsos, Alexander Optimization and Control 90C26, 90C30, 90C90, 68T01, 60-04 Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models frequently lead to large problems, they also exhibit homogeneous structures and repeating patterns (e.g., layers in ANNs). Thus, specialized solution strategies can be used for large problem classes. Recently, there have been some promising works proposing specialized reformulations using mixed-integer programming or reduced space formulations. However, further work is needed to develop more efficient solution approaches and keep up with the rapid development of new ML model architectures. |
| title | Optimization with Trained Machine Learning Models Embedded |
| topic | Optimization and Control 90C26, 90C30, 90C90, 68T01, 60-04 |
| url | https://arxiv.org/abs/2207.12722 |