{"id":20841045,"url":"https://github.com/benchopt/benchmark_tv_1d","last_synced_at":"2025-07-24T20:33:52.438Z","repository":{"id":37042758,"uuid":"472253500","full_name":"benchopt/benchmark_tv_1d","owner":"benchopt","description":"TV Denoising in 1D","archived":false,"fork":false,"pushed_at":"2024-07-24T12:17:37.000Z","size":157,"stargazers_count":2,"open_issues_count":8,"forks_count":7,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-05-08T22:05:12.317Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/benchopt.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2022-03-21T08:46:21.000Z","updated_at":"2024-07-03T09:25:35.000Z","dependencies_parsed_at":"2025-05-08T22:05:14.102Z","dependency_job_id":"8744d196-ec2e-4654-b975-50a6c8058cc8","html_url":"https://github.com/benchopt/benchmark_tv_1d","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":"benchopt/template_benchmark","purl":"pkg:github/benchopt/benchmark_tv_1d","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benchopt%2Fbenchmark_tv_1d","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benchopt%2Fbenchmark_tv_1d/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benchopt%2Fbenchmark_tv_1d/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benchopt%2Fbenchmark_tv_1d/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/benchopt","download_url":"https://codeload.github.com/benchopt/benchmark_tv_1d/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benchopt%2Fbenchmark_tv_1d/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266901997,"owners_count":24003599,"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","status":"online","status_checked_at":"2025-07-24T02:00:09.469Z","response_time":99,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2024-11-18T01:18:37.856Z","updated_at":"2025-07-24T20:33:52.367Z","avatar_url":"https://github.com/benchopt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Unidimensional Total variation (TV) Benchmark\n=============================================\n|Build Status| |Python 3.6+|\n\nThis benchmark is dedicated to solver of TV-1D regularised regression problem:\n\n$$\\\\boldsymbol{u} \\\\in \\\\underset{\\\\boldsymbol{u} \\\\in \\\\mathbb{R}^{p}}{\\\\mathrm{argmin}} f(\\\\boldsymbol{y}, A \\\\boldsymbol{u}) + g(D\\\\boldsymbol{u})$$\n\n\n- $\\\\boldsymbol{y} \\\\in \\\\mathbb{R}^{n}$ is a vector of observations or targets.\n- $A \\\\in \\\\mathbb{R}^{n \\\\times p}$ is a design matrix or forward operator.\n- $\\\\lambda \u003e 0$ is a regularization hyperparameter.\n- $f(\\\\boldsymbol{y}, A\\\\boldsymbol{u}) = \\\\sum\\\\limits\\_{k} l(y\\_{k}, (A\\\\boldsymbol{u})_{k})$ is a loss function, where $l$ can be quadratic loss as $l(y, x) = \\\\frac{1}{2} \\\\vert y - x \\\\vert_2^2$, or Huber loss as $l(y, x) = h\\_{\\\\delta} (y - x)$ defined by\n\n\n$$\nh\\_{\\\\delta}(t) = \\\\begin{cases} \\\\frac{1}{2} t^2 \u0026 \\\\mathrm{ if } \\\\vert t \\\\vert \\\\le \\\\delta \\\\\\\\ \\\\delta \\\\vert t \\\\vert - \\\\frac{1}{2} \\\\delta^2 \u0026 \\\\mathrm{ otherwise} \\\\end{cases}\n$$\n\n- $D \\\\in \\\\mathbb{R}^{(p-1) \\\\times p}$ is a finite difference operator, such that the regularised TV-1D term $g(D\\\\boldsymbol{u}) = \\\\lambda \\\\| \\\\boldsymbol{u} \\\\|_{TV}$ expressed as follows.\n\n\n$$g(D\\\\boldsymbol{u}) = \\\\lambda \\\\| D \\\\boldsymbol{u} \\\\|\\_{1} = \\\\lambda \\\\sum\\\\limits\\_{k = 1}^{p-1} \\\\vert u\\_{k+1} - u\\_{k} \\\\vert $$\n\n\nwhere n (or `n_samples`) stands for the number of samples, p (or `n_features`) stands for the number of features.\n\n\n\nInstall\n--------\n\nThis benchmark can be run using the following commands:\n\n.. code-block::\n\n   $ pip install -U benchopt\n   $ git clone https://github.com/benchopt/benchmark_tv_1d\n   $ benchopt run benchmark_tv_1d\n\nApart from the problem, options can be passed to `benchopt run`, to restrict the benchmarks to some solvers or datasets, e.g.:\n\n.. code-block::\n\n\t$ benchopt run benchmark_tv_1d --config benchmark_tv_1d/example_config.yml\n\n\nUse `benchopt run -h` for more details about these options, or visit https://benchopt.github.io/api.html.\n\n.. |Build Status| image:: https://github.com/benchopt/benchmark_tv_1d/workflows/Tests/badge.svg\n   :target: https://github.com/benchopt/benchmark_tv_1d/actions\n.. |Python 3.6+| image:: https://img.shields.io/badge/python-3.6%2B-blue\n   :target: https://www.python.org/downloads/release/python-360/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenchopt%2Fbenchmark_tv_1d","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenchopt%2Fbenchmark_tv_1d","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenchopt%2Fbenchmark_tv_1d/lists"}