Double Momentum Method for Lower-Level Constrained Bilevel Optimization
Authors: Wanli Shi, Yi Chang, Bin Gu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two applications demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Jilin University, China 2Mohamed bin Zayed University of Artificial Intelligence, UAE. |
| Pseudocode | Yes | Algorithm 1 DMLCBO |
| Open Source Code | No | The paper mentions implementing methods using Pytorch but does not provide any links to its own source code or state that it is open-source. |
| Open Datasets | Yes | In this experiment, we evaluate all the methods on the datasets MNIST, Fashion MNIST, Cod RNA, and Madelon 1. 1https://www.csie.ntu.edu.cn/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions 'training set' and 'testing set' but does not specify exact percentages, sample counts, or a detailed methodology for splitting the data into training, validation, and test sets. |
| Hardware Specification | Yes | We run all the methods 10 times on a PC with four 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions software like Pytorch and JAX but does not specify version numbers for these or any other libraries or dependencies. |
| Experiment Setup | Yes | Detailed settings are given in our Appendix. For our method, we search the step size from the set {1, 10-1, 10-2, 10-3, 10-4, 10-5}. Following the default setting in (Ji et al., 2021), we set Q = 3 and η = 0.5 for our method. In addition, we set ηk = 1/(100+k), c1 = 10 and c2 = 10 for our method. For V-PBGD, RMD-PCD, and Approx, following the setting in (Pedregosa, 2016), we set the inner iteration number at 100. We set δ = 1e-6. |