{"id":22894233,"url":"https://github.com/zib-iol/avi_at_scale","last_synced_at":"2025-03-31T22:38:31.309Z","repository":{"id":42477104,"uuid":"510780033","full_name":"ZIB-IOL/avi_at_scale","owner":"ZIB-IOL","description":"Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)","archived":false,"fork":false,"pushed_at":"2023-08-24T13:43:18.000Z","size":5456,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-07T01:32:08.939Z","etag":null,"topics":["approximate-vanishing-ideal","convex-optimization","frank-wolfe"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2207.01236","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ZIB-IOL.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATIONS.bib","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-07-05T14:51:20.000Z","updated_at":"2022-07-05T14:53:50.000Z","dependencies_parsed_at":"2025-02-07T01:30:02.114Z","dependency_job_id":"6a78a0ea-282d-42a9-9266-5d744e969bad","html_url":"https://github.com/ZIB-IOL/avi_at_scale","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2Favi_at_scale","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2Favi_at_scale/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2Favi_at_scale/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2Favi_at_scale/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZIB-IOL","download_url":"https://codeload.github.com/ZIB-IOL/avi_at_scale/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246552972,"owners_count":20795835,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["approximate-vanishing-ideal","convex-optimization","frank-wolfe"],"created_at":"2024-12-13T23:17:15.601Z","updated_at":"2025-03-31T22:38:31.302Z","avatar_url":"https://github.com/ZIB-IOL.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Approximate Vanishing Ideal Computations at Scale\n\n\n\nCode for the paper:\n[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)\n\n\n## References\nThis project is an extension of the previously published Git Repository\n[CGAVI](https://github.com/ZIB-IOL/cgavi/releases/tag/v1.0.0),\nwhich is the code corresponding to the following paper:\n\n[Wirth, E. S., \u0026 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)\n\n\n\n## Installation guide\nDownload the repository and store it in your preferred location, say ~/tmp.\n\nOpen your terminal and navigate to ~/tmp.\n\nRun the command: \n```shell script\n$ conda env create --file environment.yml\n```\nThis will create the conda environment avi_at_scale.\n\nActivate the conda environment with:\n```shell script\n$ conda activate avi_at_scale\n```\n\nRun the tests:\n```python3 script\n\u003e\u003e\u003e python3 -m unittest\n```\n\nNo errors should occur.\n\n\nExecute the experiments: \n```python3 script\n\u003e\u003e\u003e python3 experiments_all.py\n```\n\nThis will create folders named data_frames and plots, which contain subfolders containing the experiment results and \nthe plots, respectively. \n\nThe performance experiments can be displayed as latex_code by executing:\n```python3 script\n\u003e\u003e\u003e experiments_results_to_latex.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzib-iol%2Favi_at_scale","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzib-iol%2Favi_at_scale","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzib-iol%2Favi_at_scale/lists"}