{"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-sparse-group","title":"GAP Safe Screening Rules for Sparse-Group-Lasso","arxiv_id":"1602.06225","date":"2016-02-19","proceeding":null,"authors":["Eugene Ndiaye","Olivier Fercoq","Alexandre Gramfort","Joseph Salmon"],"abstract":"In high dimensional settings, sparse structures are crucial for efficiency,\neither in term of memory, computation or performance. In some contexts, it is\nnatural to handle more refined structures than pure sparsity, such as for\ninstance group sparsity. Sparse-Group Lasso has recently been introduced in the\ncontext of linear regression to enforce sparsity both at the feature level and\nat the group level. We adapt to the case of Sparse-Group Lasso recent safe\nscreening rules that discard early in the solver irrelevant features/groups.\nSuch rules have led to important speed-ups for a wide range of iterative\nmethods. Thanks to dual gap computations, we provide new safe screening rules\nfor Sparse-Group Lasso and show significant gains in term of computing time for\na coordinate descent implementation.","url_abs":"http://arxiv.org/abs/1602.06225v1","url_pdf":"http://arxiv.org/pdf/1602.06225v1.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-sparse-group","repo_url":"https://github.com/EugeneNdiaye/GAPSAFE_SGL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}