Robust and Adaptive Optimization under a Large Language Model Lens
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Bertsimas, Dimitris Margaritis, Georgios |
| author_facet | Bertsimas, Dimitris Margaritis, Georgios |
| contents | In this paper, we explore the application of ChatGPT in the domain of Robust and Adaptive Robust Optimization. We demonstrate that with appropriate prompting, ChatGPT can be used to auto-formulate and solve simple Robust and Adaptive Optimization Problems. We first develop specialized informational prompts tailored to the domains of Adaptive and Robust Optimization. Then, we show that using these prompts, ChatGPT is able to (i) formulate an optimization problem, (ii) adapt the problem so that it accounts for user-specified uncertainty, (iii) derive the computationally tractable robust counterpart of the problem and (iv) produce executable code that solves the problem. For simple Optimization Problems, we show that ChatGPT is able to perform these steps with little to no errors. We also highlight some instances of erroneous execution of the steps. Overall, we show that using in-context learning, ChatGPT has the potential to adapt to higlhy specialized and niche domains, in which it would otherwise demonstrate very poor out-of-the-box performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust and Adaptive Optimization under a Large Language Model Lens Bertsimas, Dimitris Margaritis, Georgios Optimization and Control In this paper, we explore the application of ChatGPT in the domain of Robust and Adaptive Robust Optimization. We demonstrate that with appropriate prompting, ChatGPT can be used to auto-formulate and solve simple Robust and Adaptive Optimization Problems. We first develop specialized informational prompts tailored to the domains of Adaptive and Robust Optimization. Then, we show that using these prompts, ChatGPT is able to (i) formulate an optimization problem, (ii) adapt the problem so that it accounts for user-specified uncertainty, (iii) derive the computationally tractable robust counterpart of the problem and (iv) produce executable code that solves the problem. For simple Optimization Problems, we show that ChatGPT is able to perform these steps with little to no errors. We also highlight some instances of erroneous execution of the steps. Overall, we show that using in-context learning, ChatGPT has the potential to adapt to higlhy specialized and niche domains, in which it would otherwise demonstrate very poor out-of-the-box performance. |
| title | Robust and Adaptive Optimization under a Large Language Model Lens |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.00568 |