Heavy Ball Momentum for Conditional Gradient
Authors: Bingcong Li, Alireza Sadeghi, Georgios Giannakis
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results demonstrate the usefulness of heavy ball momentum in FW iterations. |
| Researcher Affiliation | Academia | University of Minnesota Twin Cities Minneapolis, MN, USA |
| Pseudocode | Yes | Algorithm 1 FW [9] ... Algorithm 2 FW with heavy ball momentum ... Algorithm 3 FW with heavy ball momentum and restart |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | Datasets from LIBSVM2 are used in the numerical tests, where details of the datasets are deferred to Appendix F due to space limitation. 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, and test sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the algorithms and their theoretical properties but does not provide specific experimental setup details like hyperparameter values, learning rates, or batch sizes. |