{"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/a-simple-practical-accelerated-method-for","title":"A Simple Practical Accelerated Method for Finite Sums","arxiv_id":"1602.02442","date":"2016-02-08","proceeding":"NeurIPS 2016 12","authors":["Aaron Defazio"],"abstract":"We describe a novel optimization method for finite sums (such as empirical\nrisk minimization problems) building on the recently introduced SAGA method.\nOur method achieves an accelerated convergence rate on strongly convex smooth\nproblems. Our method has only one parameter (a step size), and is radically\nsimpler than other accelerated methods for finite sums. Additionally it can be\napplied when the terms are non-smooth, yielding a method applicable in many\nareas where operator splitting methods would traditionally be applied.","url_abs":"http://arxiv.org/abs/1602.02442v2","url_pdf":"http://arxiv.org/pdf/1602.02442v2.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":"a-simple-practical-accelerated-method-for","repo_url":"https://github.com/adefazio/point-saga","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"saga","method_name":"SAGA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.02442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1602.02442"}},"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/adefazio/point-saga","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"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":"16fef92e893dcca8","entry":"readLibSVM","repo":"adefazio/point-saga","repo_kind":"official","path":"datasets/read_libsvm.py","file_url":"https://github.com/adefazio/point-saga/blob/HEAD/datasets/read_libsvm.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"16fef92e893dcca8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}