{"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/breaking-the-nonsmooth-barrier-a-scalable","title":"Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization","arxiv_id":"1707.06468","date":"2017-07-20","proceeding":"NeurIPS 2017 12","authors":["Fabian Pedregosa","Rémi Leblond","Simon Lacoste-Julien"],"abstract":"Due to their simplicity and excellent performance, parallel asynchronous\nvariants of stochastic gradient descent have become popular methods to solve a\nwide range of large-scale optimization problems on multi-core architectures.\nYet, despite their practical success, support for nonsmooth objectives is still\nlacking, making them unsuitable for many problems of interest in machine\nlearning, such as the Lasso, group Lasso or empirical risk minimization with\nconvex constraints.\n  In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse\nmethod inspired by SAGA, a variance reduced incremental gradient algorithm. The\nproposed method is easy to implement and significantly outperforms the state of\nthe art on several nonsmooth, large-scale problems. We prove that our method\nachieves a theoretical linear speedup with respect to the sequential version\nunder assumptions on the sparsity of gradients and block-separability of the\nproximal term. Empirical benchmarks on a multi-core architecture illustrate\npractical speedups of up to 12x on a 20-core machine.","url_abs":"http://arxiv.org/abs/1707.06468v3","url_pdf":"http://arxiv.org/pdf/1707.06468v3.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":"breaking-the-nonsmooth-barrier-a-scalable","repo_url":"https://github.com/fabianp/ProxASAGA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"saga","method_name":"SAGA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}