{"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/celer-a-fast-solver-for-the-lasso-with-dual","title":"Celer: a Fast Solver for the Lasso with Dual Extrapolation","arxiv_id":"1802.07481","date":"2018-02-21","proceeding":"ICML 2018 7","authors":["Mathurin Massias","Alexandre Gramfort","Joseph Salmon"],"abstract":"Convex sparsity-inducing regularizations are ubiquitous in high-dimensional\nmachine learning, but solving the resulting optimization problems can be slow.\nTo accelerate solvers, state-of-the-art approaches consist in reducing the size\nof the optimization problem at hand. In the context of regression, this can be\nachieved either by discarding irrelevant features (screening techniques) or by\nprioritizing features likely to be included in the support of the solution\n(working set techniques). Duality comes into play at several steps in these\ntechniques. Here, we propose an extrapolation technique starting from a\nsequence of iterates in the dual that leads to the construction of improved\ndual points. This enables a tighter control of optimality as used in stopping\ncriterion, as well as better screening performance of Gap Safe rules. Finally,\nwe propose a working set strategy based on an aggressive use of Gap Safe\nscreening rules. Thanks to our new dual point construction, we show significant\ncomputational speedups on multiple real-world problems.","url_abs":"http://arxiv.org/abs/1802.07481v3","url_pdf":"http://arxiv.org/pdf/1802.07481v3.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":"celer-a-fast-solver-for-the-lasso-with-dual","repo_url":"https://github.com/mathurinm/celer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.07481","atlas_url":"https://app.syntology.ai/?focus=1802.07481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07481"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/mathurinm/celer","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"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":0,"samples":[{"code_sha256_prefix":"ba985eb63a20192d","entry":"build_dataset","repo":"mathurinm/celer","repo_kind":"official","path":"celer/utils/testing.py","file_url":"https://github.com/mathurinm/celer/blob/HEAD/celer/utils/testing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"ba985eb63a20192d"}},{"code_sha256_prefix":"c7c0a4bdd0925c59","entry":"celer_path","repo":"mathurinm/celer","repo_kind":"official","path":"celer/homotopy.py","file_url":"https://github.com/mathurinm/celer/blob/HEAD/celer/homotopy.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c7c0a4bdd0925c59"}},{"code_sha256_prefix":"bdea1f2fd2b23e9d","entry":"dnorm_enet","repo":"mathurinm/celer","repo_kind":"official","path":"celer/homotopy.py","file_url":"https://github.com/mathurinm/celer/blob/HEAD/celer/homotopy.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"bdea1f2fd2b23e9d"}},{"code_sha256_prefix":"0ffc0daf42533288","entry":"make_correlated_data","repo":"mathurinm/celer","repo_kind":"official","path":"celer/datasets/simulated.py","file_url":"https://github.com/mathurinm/celer/blob/HEAD/celer/datasets/simulated.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"0ffc0daf42533288"}},{"code_sha256_prefix":"1ad063747cc74d0a","entry":"mtl_path","repo":"mathurinm/celer","repo_kind":"official","path":"celer/homotopy.py","file_url":"https://github.com/mathurinm/celer/blob/HEAD/celer/homotopy.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1ad063747cc74d0a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}