Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Cut Generating Functions for Integer Programming
Authors: Hongyu Cheng, Amitabh Basu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results show that the selected CGF can outperform the GMI cuts for certain distributions. |
| Researcher Affiliation | Academia | Hongyu Cheng Dept. of Applied Mathematics & Statistics Johns Hopkins University Baltimore, MD 21218 EMAIL Amitab h Basu Dept. of Applied Mathematics & Statistics Johns Hopkins University Baltimore, MD 21218 EMAIL |
| Pseudocode | Yes | Algorithm 1 shows how to compute the function values πf,µ(r) (the cutting plane coefficients), in time that is linear in the dimension k. |
| Open Source Code | Yes | The code and data used in all experiments are available at https://github.com/Hongyu-Cheng/Learn CGF. |
| Open Datasets | Yes | Instances were generated using the so-called Chvátal distribution from [Balcan et al., 2021b]... The packing instances were generated using the distribution from [Tang et al., 2020]. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly describe a separate validation set or its split details. |
| Hardware Specification | Yes | The experiments were run on a Linux machine equipped with a 12-core Intel i7-12700F CPU and 32GB of RAM. |
| Software Dependencies | Yes | We solved the integer programming problems using Gurobi 11.0.1 [Gurobi Optimization, LLC, 2023], with default cuts, heuristics, and presolve settings turned off. |
| Experiment Setup | Yes | The parameters for the cut generating functions were selected to minimize the average branch-and-cut tree size on the training set of size 100... We fixed p = q = 2 and performed a grid search with a step size of 0.1 to select the best parameter µ {0, 0.1, . . . , 0.9, 1}2... We uniformly sampled 121 different µ on the simplex k... All cuts are generated from the first row of the simplex tableau with a non-integer right-hand side, and the k-row cut is generated from the first k such rows. |