Learning to Remove Cuts in Integer Linear Programming
Authors: Pol Puigdemont, Stratis Skoulakis, Grigorios Chrysos, Volkan Cevher
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We divide our experiments in two main parts. The first one focuses on evaluating the performance of cut removal acting against multiple benchmark policies by rolling them out on synthetic test MILP instances for each of the problem families in a controlled environment. Next, we investigate how well do our trained models generalize to larger instances. |
| Researcher Affiliation | Academia | 1LIONS, Ecole Polytechnique F ed erale de Lausanne, Switzerland 2Work developed during an exchange coming from Universitat Polit ecnica de Catalunya (UPC), Spain 3Department of Electrical and Computer Engineering, University of Wisconsin-Madison, USA. |
| Pseudocode | Yes | Algorithm 1 Cutting plane method Algorithm 2 Cutting Plane method with Cut Removal |
| Open Source Code | No | The paper does not provide a direct link to its source code or explicitly state that the code is publicly released. |
| Open Datasets | No | The paper describes how it generates its own instances for different problem families (e.g., 'For set cover we suggest our own probabilistic formulation. Details on the generation of the instances can be found in the Appendix B.1.'). It does not provide concrete access information (link, DOI, specific repository) to a pre-existing publicly available dataset. |
| Dataset Splits | Yes | For each problem family, we use 2000 instances for training, 500 instances for validation and 500 instances for testing as done in Paulus et al. (2022). |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions general 'compute resources'. |
| Software Dependencies | Yes | In order to stress-test our environment and compute the optimal solution required to obtain the IGC metrics we use the SCIP solver (Bestuzheva et al., 2023). |
| Experiment Setup | Yes | We trained our models with SGD with a lr of 5 10 3 for 50 epochs using a batch size of 104 with a patience parameter of 5. |