Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917976788172800 |
|---|---|
| author | Chen, Yanguang Gao, Wenzhi Zhang, Wanyu Ge, Dongdong Liu, Huikang Ye, Yinyu |
| author_facet | Chen, Yanguang Gao, Wenzhi Zhang, Wanyu Ge, Dongdong Liu, Huikang Ye, Yinyu |
| contents | In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models. Our method is based on a data-driven multi-variable cardinality branching procedure that splits the MIP feasible region using hyperplanes chosen by the concentration inequalities. Unlike most previous ML+MIP approaches that either require complicated implementation or suffer from a lack of theoretical justification, our method is simple, flexible, provable, and explainable. Numerical experiments on both classical OR benchmark datasets and real-life instances validate the efficiency of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12352 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching Chen, Yanguang Gao, Wenzhi Zhang, Wanyu Ge, Dongdong Liu, Huikang Ye, Yinyu Optimization and Control Machine Learning In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models. Our method is based on a data-driven multi-variable cardinality branching procedure that splits the MIP feasible region using hyperplanes chosen by the concentration inequalities. Unlike most previous ML+MIP approaches that either require complicated implementation or suffer from a lack of theoretical justification, our method is simple, flexible, provable, and explainable. Numerical experiments on both classical OR benchmark datasets and real-life instances validate the efficiency of our proposed method. |
| title | Data-driven Mixed Integer Optimization through Probabilistic Multi-variable Branching |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2305.12352 |