Learning to Solve Bilevel Programs with Binary Tender
Authors: Bo Zhou, Ruiwei Jiang, Siqian Shen
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of these approaches through extensive numerical experiments, whose lower-level problems are linear and mixed-integer programs, respectively. |
| Researcher Affiliation | Academia | Bo Zhou, Ruiwei Jiang, Siqian Shen Department of Industrial and Operations Engineering University of Michigan, Ann Arbor, MI 48109, USA {bozum, ruiwei, siqian}@umich.edu |
| Pseudocode | Yes | Algorithm 1 Enhanced Sampling |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | We randomly generate 6 classes of instances for numerical experiments, with n = 10, 20, 30, 40, 50, 60. The details of instance generation is reported in Appendix C. |
| Dataset Splits | No | The paper states 'By default, we generate 1,000 samples using Algorithm 1 and use them in training neural networks,' but does not provide specific train/validation/test dataset splits or mention a validation set explicitly used for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of the 'Adam algorithm' and comparison with the 'Mi BS' solver, but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | We adopt Re LU as the activation function and design the architecture of neural networks by Proposition 1 and by Propositions 3 4 for GNN and ISNN, respectively. We use the Adam algorithm (Kingma & Ba, 2014) for training for 1000 epochs and set the learning rate as 0.001 with the decay 0.001. |