https://github.com/zib-iol/avi_at_scale
Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
https://github.com/zib-iol/avi_at_scale
approximate-vanishing-ideal convex-optimization frank-wolfe
Last synced: about 2 months ago
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Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
- Host: GitHub
- URL: https://github.com/zib-iol/avi_at_scale
- Owner: ZIB-IOL
- License: mit
- Created: 2022-07-05T14:51:20.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-24T13:43:18.000Z (over 1 year ago)
- Last Synced: 2025-02-07T01:32:08.939Z (3 months ago)
- Topics: approximate-vanishing-ideal, convex-optimization, frank-wolfe
- Language: Python
- Homepage: https://arxiv.org/abs/2207.01236
- Size: 5.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATIONS.bib
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README
# Approximate Vanishing Ideal Computations at Scale
Code for the paper:
[Wirth, E.S., Kera, H. and Pokutta, S., 2022, September. Approximate Vanishing Ideal Computations at Scale. In Proceedings of the Eleventh International Conference on Learning Representations.](https://openreview.net/forum?id=3ZPESALKXO)
## References
This project is an extension of the previously published Git Repository
[CGAVI](https://github.com/ZIB-IOL/cgavi/releases/tag/v1.0.0),
which is the code corresponding to the following paper:
[Wirth, E. S., & Pokutta, S. (2022, May). Conditional gradients for the approximately vanishing ideal. In Proceedings of the International Conference on Artificial Intelligence and Statistics (pp. 2191-2209). PMLR.](https://proceedings.mlr.press/v151/wirth22a.html)
## Installation guide
Download the repository and store it in your preferred location, say ~/tmp.
Open your terminal and navigate to ~/tmp.
Run the command:
```shell script
$ conda env create --file environment.yml
```
This will create the conda environment avi_at_scale.
Activate the conda environment with:
```shell script
$ conda activate avi_at_scale
```
Run the tests:
```python3 script
>>> python3 -m unittest
```
No errors should occur.
Execute the experiments:
```python3 script
>>> python3 experiments_all.py
```
This will create folders named data_frames and plots, which contain subfolders containing the experiment results and
the plots, respectively.
The performance experiments can be displayed as latex_code by executing:
```python3 script
>>> experiments_results_to_latex.py
```