Conformal Prediction for Early Stopping in Mixed Integer Optimization
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914615173054464 |
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| author | Clarke, Stefan Stellato, Bartolomeo |
| author_facet | Clarke, Stefan Stellato, Bartolomeo |
| contents | Mixed-integer optimization solvers often find optimal solutions early in the search, yet spend the majority of computation time proving optimality. We exploit this by learning when to terminate solvers early on distributions of similar problem instances. Our method trains a neural network to estimate the true optimality gap from the solver state, then uses conformal prediction to calibrate a stopping threshold with rigorous probabilistic guarantees on solution quality. On five problem families from the distributional MIPLIB library, our method reduces solve time by over 60% while guaranteeing 0.1%- optimal solutions with 95% probability |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01476 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction for Early Stopping in Mixed Integer Optimization Clarke, Stefan Stellato, Bartolomeo Optimization and Control Mixed-integer optimization solvers often find optimal solutions early in the search, yet spend the majority of computation time proving optimality. We exploit this by learning when to terminate solvers early on distributions of similar problem instances. Our method trains a neural network to estimate the true optimality gap from the solver state, then uses conformal prediction to calibrate a stopping threshold with rigorous probabilistic guarantees on solution quality. On five problem families from the distributional MIPLIB library, our method reduces solve time by over 60% while guaranteeing 0.1%- optimal solutions with 95% probability |
| title | Conformal Prediction for Early Stopping in Mixed Integer Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2602.01476 |