{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gap-safe-screening-rules-for-sparsity","title":"Gap Safe screening rules for sparsity enforcing penalties","arxiv_id":"1611.05780","date":"2016-11-17","proceeding":null,"authors":["Eugene Ndiaye","Olivier Fercoq","Alexandre Gramfort","Joseph Salmon"],"abstract":"In high dimensional regression settings, sparsity enforcing penalties have\nproved useful to regularize the data-fitting term. A recently introduced\ntechnique called screening rules propose to ignore some variables in the\noptimization leveraging the expected sparsity of the solutions and consequently\nleading to faster solvers. When the procedure is guaranteed not to discard\nvariables wrongly the rules are said to be safe. In this work, we propose a\nunifying framework for generalized linear models regularized with standard\nsparsity enforcing penalties such as $\\ell_1$ or $\\ell_1/\\ell_2$ norms. Our\ntechnique allows to discard safely more variables than previously considered\nsafe rules, particularly for low regularization parameters. Our proposed Gap\nSafe rules (so called because they rely on duality gap computation) can cope\nwith any iterative solver but are particularly well suited to (block)\ncoordinate descent methods. Applied to many standard learning tasks, Lasso,\nSparse-Group Lasso, multi-task Lasso, binary and multinomial logistic\nregression, etc., we report significant speed-ups compared to previously\nproposed safe rules on all tested data sets.","url_abs":"http://arxiv.org/abs/1611.05780v4","url_pdf":"http://arxiv.org/pdf/1611.05780v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gap-safe-screening-rules-for-sparsity","repo_url":"https://github.com/EugeneNdiaye/Gap_Safe_Rules","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1611.05780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.05780"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/EugeneNdiaye/Gap_Safe_Rules","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"844d6680b6e2770c","entry":"lasso_path","repo":"EugeneNdiaye/Gap_Safe_Rules","repo_kind":"listed","path":"gsroptim/lasso.py","file_url":"https://github.com/EugeneNdiaye/Gap_Safe_Rules/blob/HEAD/gsroptim/lasso.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"844d6680b6e2770c"}},{"code_sha256_prefix":"42ab5a65bcc301c6","entry":"logreg_path","repo":"EugeneNdiaye/Gap_Safe_Rules","repo_kind":"listed","path":"gsroptim/logreg.py","file_url":"https://github.com/EugeneNdiaye/Gap_Safe_Rules/blob/HEAD/gsroptim/logreg.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"42ab5a65bcc301c6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}