{"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/comparing-different-subgradient-methods-for","title":"Comparing different subgradient methods for solving convex optimization problems with functional constraints","arxiv_id":"2101.01045","date":"2021-01-04","proceeding":null,"authors":["Thi Lan Dinh","Ngoc Hoang Anh Mai"],"abstract":"We consider the problem of minimizing a convex, nonsmooth function subject to a closed convex constraint domain. The methods that we propose are reforms of subgradient methods based on Metel--Takeda's paper [Optimization Letters 15.4 (2021): 1491-1504] and Boyd's works [Lecture notes of EE364b, Stanford University, Spring 2013-14, pp. 1-39]. While the former has complexity $\\mathcal{O}(\\varepsilon^{-2r})$ for all $r> 1$, the complexity of the latter is $\\mathcal{O}(\\varepsilon^{-2})$. We perform some comparisons between these two methods using several test examples.","url_abs":"https://arxiv.org/abs/2101.01045v2","url_pdf":"https://arxiv.org/pdf/2101.01045v2.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":"comparing-different-subgradient-methods-for","repo_url":"https://github.com/dinhthilan/cop","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}