Learning to Branch in Mixed Integer Programming
Authors: Elias Khalil, Pierre Le Bodic, Le Song, George Nemhauser, Bistra Dilkina
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver. |
| Researcher Affiliation | Academia | 1School of Computational Science & Engineering 2School of Industrial & Systems Engineering Georgia Institute of Technology |
| Pseudocode | No | The paper describes its framework in three phases (Data collection, Model learning, ML-based branching) but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the Benchmark set from MIPLIB2010 as our test set; we refer to (Koch et al. 2011) for details. |
| Dataset Splits | No | The paper mentions collecting data for a "training dataset" and using MIPLIB2010 as a "test set," but it does not specify explicit training, validation, and test splits with percentages or sample counts, nor does it mention a dedicated validation set. |
| Hardware Specification | Yes | All experiments were run on a cluster of four 64-core machines with AMD 2.4 GHz processors and 264 GB of memory; each run was limited to 2 GB of memory |
| Software Dependencies | Yes | We use the C API of IBM ILOG CPLEX 12.6.1 |
| Experiment Setup | Yes | We use α = 0.2 and SVMrank with a trade-off parameter C = 0.1 between training error and margin (λ in (4) is a function of C). We varied α {0.1, 0.2, 0.3, 0.4, 0.5} and C {0.001, 0.01, 0.1, 1} (0.01 is the default in SVMrank), and found that SB+ML performs similarly. For SB, SB+PC and SB+ML, κ is set to 10, and all SB calls are limited to 50 dual simplex iterations |