{"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/scope-scalable-composite-optimization-for","title":"SCOPE: Scalable Composite Optimization for Learning on Spark","arxiv_id":"1602.00133","date":"2016-01-30","proceeding":null,"authors":["Shen-Yi Zhao","Ru Xiang","Ying-Hao Shi","Peng Gao","Wu-Jun Li"],"abstract":"Many machine learning models, such as logistic regression~(LR) and support\nvector machine~(SVM), can be formulated as composite optimization problems.\nRecently, many distributed stochastic optimization~(DSO) methods have been\nproposed to solve the large-scale composite optimization problems, which have\nshown better performance than traditional batch methods. However, most of these\nDSO methods are not scalable enough. In this paper, we propose a novel DSO\nmethod, called \\underline{s}calable \\underline{c}omposite\n\\underline{op}timization for l\\underline{e}arning~({SCOPE}), and implement it\non the fault-tolerant distributed platform \\mbox{Spark}. SCOPE is both\ncomputation-efficient and communication-efficient. Theoretical analysis shows\nthat SCOPE is convergent with linear convergence rate when the objective\nfunction is convex. Furthermore, empirical results on real datasets show that\nSCOPE can outperform other state-of-the-art distributed learning methods on\nSpark, including both batch learning methods and DSO methods.","url_abs":"http://arxiv.org/abs/1602.00133v5","url_pdf":"http://arxiv.org/pdf/1602.00133v5.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":"scope-scalable-composite-optimization-for","repo_url":"https://github.com/LIBBLE/LIBBLE-Spark","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}