Name
Abstract | ||
|---|---|---|
Mixed Integer Programming (MIP) is one of the most widely used modeling techniques to deal with combinatorial optimization problems. In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients. This offers the opportunity for machine learning method to explore the correlations between model structures and the resulting solution values. To address this issue, we propose to represent an MIP instance using a tripartite graph, based on which a Graph Convolutional Network (GCN) is constructed to predict solution values for binary variables. The predicted solutions are used to generate a local branching cut to the model which accelerate the solution process for MIP. Computational evaluations on 8 distinct types of MIP problems show that the proposed framework improves the performance of a state-of-the-art open source MIP solver significantly in terms of running time and solution quality. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.09575 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Ya Ding | 1 | 0 | 1.01 |
| Chao Zhang | 2 | 351 | 63.97 |
| Lei Shen | 3 | 0 | 0.68 |
| Shengyin Li | 4 | 0 | 1.01 |
| Bing Wang | 5 | 38 | 20.03 |
| Yinghui Xu | 6 | 172 | 20.23 |
| Le Song | 7 | 2437 | 159.27 |