{"id":18339303,"url":"https://github.com/mfschubert/mewtax","last_synced_at":"2025-04-09T20:37:04.954Z","repository":{"id":218148249,"uuid":"742055426","full_name":"mfschubert/mewtax","owner":"mfschubert","description":"Differentiable minimization in jax using Newton's method","archived":false,"fork":false,"pushed_at":"2024-06-14T19:53:05.000Z","size":28,"stargazers_count":1,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T12:47:45.983Z","etag":null,"topics":["jax","optimization"],"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/mfschubert.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-01-11T17:08:29.000Z","updated_at":"2025-01-17T16:19:31.000Z","dependencies_parsed_at":"2024-11-05T20:17:21.651Z","dependency_job_id":"03d6d17d-3370-4cba-a4b7-0609d623c363","html_url":"https://github.com/mfschubert/mewtax","commit_stats":null,"previous_names":["mfschubert/mewtax"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mfschubert%2Fmewtax","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mfschubert%2Fmewtax/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mfschubert%2Fmewtax/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mfschubert%2Fmewtax/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mfschubert","download_url":"https://codeload.github.com/mfschubert/mewtax/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248108725,"owners_count":21049185,"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":["jax","optimization"],"created_at":"2024-11-05T20:17:10.400Z","updated_at":"2025-04-09T20:37:04.907Z","avatar_url":"https://github.com/mfschubert.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Differentiable minimization in jax using Newton's method\n`v0.0.1`\n\nThis project essentially repackages code from the [implicit layers tutorial](https://implicit-layers-tutorial.org/implicit_functions/) to provide a `minimize_newton` function.\n\nGiven a function `fn(params, z)`, it finds the `z_star` which minimizes `fn` for given `params`. Further, the gradient of the solution with respect to `params` can be computed; this is done using a custom vjp rule, as shown in the tutorial.\n\n## Installation\n\nmewtax can be installed via pip:\n```\npip install mewtax\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmfschubert%2Fmewtax","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmfschubert%2Fmewtax","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmfschubert%2Fmewtax/lists"}