ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911602348916736 |
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| author | Cai, Junyang Raqabi, El Mehdi Er Van Hentenryck, Pascal Dilkina, Bistra |
| author_facet | Cai, Junyang Raqabi, El Mehdi Er Van Hentenryck, Pascal Dilkina, Bistra |
| contents | Mixed-Integer Linear Programs (MIPs) are powerful and flexible tools for modeling a wide range of real-world combinatorial optimization problems. Predict-and-Search methods operate by using a predictive model to estimate promising variable assignments and then guiding a search procedure toward high-quality solutions. Recent research has demonstrated that incorporating machine learning (ML) into the Predict-and-Search framework significantly enhances its performance. Still, it is restricted to binary-only problems and overlooks the presence of fixed variable structures that commonly arise in real-world settings. This work extends the current Predict-and-Search (PAS) framework to parametric general parametric MIPs and introduces ID-PAS+, an identity-aware learning framework that enables the ML model to handle heterogeneous variable types more effectively. Experiments on several real-world large-scale problems demonstrate that ID-PAS+ consistently achieves superior performance compared to the state-of-the-art solver Gurobi and PAS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs Cai, Junyang Raqabi, El Mehdi Er Van Hentenryck, Pascal Dilkina, Bistra Artificial Intelligence Mixed-Integer Linear Programs (MIPs) are powerful and flexible tools for modeling a wide range of real-world combinatorial optimization problems. Predict-and-Search methods operate by using a predictive model to estimate promising variable assignments and then guiding a search procedure toward high-quality solutions. Recent research has demonstrated that incorporating machine learning (ML) into the Predict-and-Search framework significantly enhances its performance. Still, it is restricted to binary-only problems and overlooks the presence of fixed variable structures that commonly arise in real-world settings. This work extends the current Predict-and-Search (PAS) framework to parametric general parametric MIPs and introduces ID-PAS+, an identity-aware learning framework that enables the ML model to handle heterogeneous variable types more effectively. Experiments on several real-world large-scale problems demonstrate that ID-PAS+ consistently achieves superior performance compared to the state-of-the-art solver Gurobi and PAS. |
| title | ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10211 |