{"id":18311402,"url":"https://github.com/fabian-sp/snspp","last_synced_at":"2025-04-05T18:31:53.410Z","repository":{"id":40302722,"uuid":"263936179","full_name":"fabian-sp/snspp","owner":"fabian-sp","description":"Stochastic Proximal Point algorithm for regularized statistical learning","archived":false,"fork":false,"pushed_at":"2024-02-19T07:55:28.000Z","size":224329,"stargazers_count":4,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-21T08:35:01.241Z","etag":null,"topics":["optimization","statistical-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fabian-sp.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}},"created_at":"2020-05-14T14:21:43.000Z","updated_at":"2024-07-18T08:43:20.000Z","dependencies_parsed_at":"2024-01-22T13:03:42.633Z","dependency_job_id":null,"html_url":"https://github.com/fabian-sp/snspp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2Fsnspp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2Fsnspp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2Fsnspp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabian-sp%2Fsnspp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fabian-sp","download_url":"https://codeload.github.com/fabian-sp/snspp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247383904,"owners_count":20930368,"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":["optimization","statistical-learning"],"created_at":"2024-11-05T16:17:34.638Z","updated_at":"2025-04-05T18:31:48.390Z","avatar_url":"https://github.com/fabian-sp.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SNSPP\n\n[![arXiv](https://img.shields.io/badge/arXiv-2204.00406-b31b1b.svg)](https://arxiv.org/abs/2204.00406)\n\n`SNSPP` is a semismooth Newton stochastic proximal point method with variance reduction. The `SNSPP` method is implemented in [`snspp/solver/spp_solver`](/snspp/solver/spp_solver.py) and references therein.\n\n\n## Introduction\n\nWe aim for solving problems of the form\n\n\u003cimg src=\"https://latex.codecogs.com/gif.latex?\\min_xf(x)+\\varphi(x)\" title=\"problem formulation\"/\u003e\n\nwhere the first part of the objective has the special form\n\n\u003cimg src=\"https://latex.codecogs.com/gif.latex?f(x)=\\frac{1}{N}\\sum_{i=1}^{N}f_i(A_ix)\" title=\"f structure\"/\u003e\n\nThis problem structure is common in statistical learning problems: each summand of `f` is the loss at one data sample and `phi` is a (convex), possibly nonsmooth regularizer. Note that for optimal performance `f` and `phi` should be [Numba jitted classes](https://numba.pydata.org/numba-doc/dev/user/jitclass.html).\n\n## Getting started\n\nInstall via \n\n    python setup.py\n\nor in order to install in developer mode via\n\n    python setup.py clean --all develop clean --all\n\n\n## Requirements\n\nThe required packages are listed in `requirements.txt`. Here we list the versions of the most important packages that were used.\n\n\n```\nnumpy==1.21.5\nnumba==0.55.1\nsklearn==1.1.2\nscipy==1.9.1\npandas==1.4.4\nmatplotlib==3.5.2\nseaborn==0.11.2\ncsr==0.4.1\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabian-sp%2Fsnspp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffabian-sp%2Fsnspp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabian-sp%2Fsnspp/lists"}