Learning to Configure Separators in Branch-and-Cut
Authors: Sirui Li, Wenbin Ouyang, Max Paulus, Cathy Wu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive computational experiments demonstrate that our method achieves significant speedup over the competitive MILP solver SCIP on a variety of benchmark MILP datasets and objectives. |
| Researcher Affiliation | Academia | MIT siruil@mit.edu Wenbin Ouyang* MIT oywenbin@mit.edu Max B. Paulus ETH Zürich max.paulus@inf.ethz.ch MIT cathywu@mit.edu |
| Pseudocode | Yes | The detailed algorithm and discussions of the filtering and termination procedure are provided in Appendix A.3. ... The detailed algorithm is provided in Alg. 2 of Appendix A.4. ... We provide the complete training procedure in Alg. 3 of Appendix A.5. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/mit-wu-lab/learning-to-configure-separators. |
| Open Datasets | Yes | We divide the experiment section into two main parts. First, we evaluate our method on standard MILP benchmarks from Tang et al. [51] and Ecole [43], where the number of variables and constraints range from 60 to 10, 000. ... Second, we examine the efficacy of our method by applying it to large-scale real-world MILP benchmarks, including the MIPLIB [20], NN Verification [40], and Load Balancing in the ML4CO challenges [19]. |
| Dataset Splits | Yes | By default, we generate a training set Ksmall of 100 instances for configuration space restriction, another training set Klarge of 800 for predictor network training, a validation set of 100 instances, and a test set of 100 instances for each class Appendix A.6 provides full details of the setup. |
| Hardware Specification | No | The paper mentions "48 CPU processes" for reward label collection and HPC resources (MIT Super Cloud and Lincoln Laboratory Supercomputing Center) but does not provide specific CPU models, GPU models, or detailed hardware specifications. |
| Software Dependencies | No | The paper mentions using SCIP and Gurobi solvers, and PySCIPOpt, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train the networks with ADAM [32] under a learning rate of 10^-3. The reward label collection is performed via multi-processing with 48 CPU processes. As in previous works [51, 42, 54], we train separate models for each MILP class. |