{"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/distributed-optimization-with-arbitrary-local","title":"Distributed Optimization with Arbitrary Local Solvers","arxiv_id":"1512.04039","date":"2015-12-13","proceeding":null,"authors":["Chenxin Ma","Jakub Konečný","Martin Jaggi","Virginia Smith","Michael. I. Jordan","Peter Richtárik","Martin Takáč"],"abstract":"With the growth of data and necessity for distributed optimization methods,\nsolvers that work well on a single machine must be re-designed to leverage\ndistributed computation. Recent work in this area has been limited by focusing\nheavily on developing highly specific methods for the distributed environment.\nThese special-purpose methods are often unable to fully leverage the\ncompetitive performance of their well-tuned and customized single machine\ncounterparts. Further, they are unable to easily integrate improvements that\ncontinue to be made to single machine methods. To this end, we present a\nframework for distributed optimization that both allows the flexibility of\narbitrary solvers to be used on each (single) machine locally, and yet\nmaintains competitive performance against other state-of-the-art\nspecial-purpose distributed methods. We give strong primal-dual convergence\nrate guarantees for our framework that hold for arbitrary local solvers. We\ndemonstrate the impact of local solver selection both theoretically and in an\nextensive experimental comparison. Finally, we provide thorough implementation\ndetails for our framework, highlighting areas for practical performance gains.","url_abs":"http://arxiv.org/abs/1512.04039v2","url_pdf":"http://arxiv.org/pdf/1512.04039v2.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":"distributed-optimization-with-arbitrary-local","repo_url":"https://github.com/optml/CoCoA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"distributed-optimization","task_name":"Distributed Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.04039","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}