{"id":22894231,"url":"https://github.com/zib-iol/cgavi","last_synced_at":"2025-03-31T22:38:30.547Z","repository":{"id":65842830,"uuid":"454047028","full_name":"ZIB-IOL/CGAVI","owner":"ZIB-IOL","description":"Code for the paper: Wirth, E.S. and Pokutta, S., 2022, May. Conditional gradients for the approximately vanishing ideal. In International Conference on Artificial Intelligence and Statistics (pp. 2191-2209). PMLR. ","archived":false,"fork":false,"pushed_at":"2023-08-24T13:27:50.000Z","size":18057,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-02-07T01:32:09.078Z","etag":null,"topics":["frank-wolfe","frank-wolfe-method","vanishing-ideal"],"latest_commit_sha":null,"homepage":"","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":"2022-01-31T14:52:45.000Z","updated_at":"2024-04-24T09:05:11.000Z","dependencies_parsed_at":"2025-02-07T01:39:57.228Z","dependency_job_id":null,"html_url":"https://github.com/ZIB-IOL/CGAVI","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2FCGAVI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2FCGAVI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2FCGAVI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZIB-IOL%2FCGAVI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZIB-IOL","download_url":"https://codeload.github.com/ZIB-IOL/CGAVI/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":["frank-wolfe","frank-wolfe-method","vanishing-ideal"],"created_at":"2024-12-13T23:17:15.514Z","updated_at":"2025-03-31T22:38:30.542Z","avatar_url":"https://github.com/ZIB-IOL.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Conditional Gradients for the Approximate Vanishing Ideal\n\nCode for the paper:\n[Wirth, E. S., \u0026 Pokutta, S. (2022, May). Conditional gradients for the approximately vanishing ideal.\nIn Proceedings of the International Conference on Artificial Intelligence and Statistics (pp. 2191-2209).\nPMLR.](https://proceedings.mlr.press/v151/wirth22a.html)\n\nand\n\n[Wirth, E. and Pokutta, S., 2022. Conditional Gradients for the Approximate Vanishing Ideal.\narXiv preprint arXiv:2202.03349.](https://arxiv.org/abs/2202.03349)\n\n\n## References\nThis project is an extension of the previously published release and Git repository\n[cgavi](https://github.com/ZIB-IOL/cgavi/releases/tag/v1.0.0) and\n[avi_at_scale](https://github.com/ZIB-IOL/avi_at_scale),\nrespectively.\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 cgavi.\n\nActivate the conda environment with:\n```shell script\n$ conda activate cgavi\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_cgavi.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_to_latex_cgavi.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzib-iol%2Fcgavi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzib-iol%2Fcgavi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzib-iol%2Fcgavi/lists"}