{"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/l1-regularized-distributed-optimization-a","title":"L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework","arxiv_id":"1512.04011","date":"2015-12-13","proceeding":null,"authors":["Virginia Smith","Simone Forte","Michael. I. Jordan","Martin Jaggi"],"abstract":"Despite the importance of sparsity in many large-scale applications, there\nare few methods for distributed optimization of sparsity-inducing objectives.\nIn this paper, we present a communication-efficient framework for\nL1-regularized optimization in the distributed environment. By viewing\nclassical objectives in a more general primal-dual setting, we develop a new\nclass of methods that can be efficiently distributed and applied to common\nsparsity-inducing models, such as Lasso, sparse logistic regression, and\nelastic net-regularized problems. We provide theoretical convergence guarantees\nfor our framework, and demonstrate its efficiency and flexibility with a\nthorough experimental comparison on Amazon EC2. Our proposed framework yields\nspeedups of up to 50x as compared to current state-of-the-art methods for\ndistributed L1-regularized optimization.","url_abs":"http://arxiv.org/abs/1512.04011v2","url_pdf":"http://arxiv.org/pdf/1512.04011v2.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":"l1-regularized-distributed-optimization-a","repo_url":"https://github.com/gingsmith/proxcocoa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"l1-regularized-distributed-optimization-a","repo_url":"https://github.com/gingsmith/cocoa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"distributed-optimization","task_name":"Distributed 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}