Compact Optimality Verification for Optimization Proxies
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909214073421824 |
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| author | Chen, Wenbo Zhao, Haoruo Tanneau, Mathieu Van Hentenryck, Pascal |
| author_facet | Chen, Wenbo Zhao, Haoruo Tanneau, Mathieu Van Hentenryck, Pascal |
| contents | Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions. Following recent work by (Nellikkath & Chatzivasileiadis, 2021), this paper reconsiders the optimality verification problem for optimization proxies, i.e., the determination of the worst-case optimality gap over the instance distribution. The paper proposes a compact formulation for optimality verification and a gradient-based primal heuristic that brings substantial computational benefits to the original formulation. The compact formulation is also more general and applies to non-convex optimization problems. The benefits of the compact formulation are demonstrated on large-scale DC Optimal Power Flow and knapsack problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_21023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compact Optimality Verification for Optimization Proxies Chen, Wenbo Zhao, Haoruo Tanneau, Mathieu Van Hentenryck, Pascal Optimization and Control Artificial Intelligence Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions. Following recent work by (Nellikkath & Chatzivasileiadis, 2021), this paper reconsiders the optimality verification problem for optimization proxies, i.e., the determination of the worst-case optimality gap over the instance distribution. The paper proposes a compact formulation for optimality verification and a gradient-based primal heuristic that brings substantial computational benefits to the original formulation. The compact formulation is also more general and applies to non-convex optimization problems. The benefits of the compact formulation are demonstrated on large-scale DC Optimal Power Flow and knapsack problems. |
| title | Compact Optimality Verification for Optimization Proxies |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2405.21023 |