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

Science Score: 31.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
○
.zenodo.json file
○
DOI references
○
Academic publication links
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (6.7%) to scientific vocabulary

Keywords

approximate-vanishing-ideal convex-optimization frank-wolfe

Last synced: 4 months ago · JSON representation ·

Repository

Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)

Basic Info

Host: GitHub
Owner: ZIB-IOL
License: mit
Language: Python
Default Branch: main
Homepage: https://arxiv.org/abs/2207.01236
Size: 5.2 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 1

Topics

approximate-vanishing-ideal convex-optimization frank-wolfe

Created over 3 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

README.md

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.

References

This project is an extension of the previously published Git Repository CGAVI, 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.

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 aviatscale.

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

experimentsresultsto_latex.py ```

Owner

Name: IOL Lab
Login: ZIB-IOL
Kind: organization
Location: Germany

Website: https://iol.zib.de
Repositories: 27
Profile: https://github.com/ZIB-IOL

Working on optimization and learning at the intersection of mathematics and computer science

Citation (CITATIONS.bib)

@inproceedings{
wirth2023approximate,
title={Approximate Vanishing Ideal Computations at Scale},
author={Elias Wirth and Hiroshi Kera and Sebastian Pokutta},
booktitle={The Eleventh International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=3ZPESALKXO}
}

GitHub Events

Total

Last Year

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 0
Total pull requests: 4
Average time to close issues: N/A
Average time to close pull requests: less than a minute
Total issue authors: 0
Total pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 4
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science