{"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/catalyst-acceleration-for-first-order-convex","title":"Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice","arxiv_id":"1712.05654","date":"2017-12-15","proceeding":null,"authors":["Hongzhou Lin","Julien Mairal","Zaid Harchaoui"],"abstract":"We introduce a generic scheme for accelerating gradient-based optimization\nmethods in the sense of Nesterov. The approach, called Catalyst, builds upon\nthe inexact accelerated proximal point algorithm for minimizing a convex\nobjective function, and consists of approximately solving a sequence of\nwell-chosen auxiliary problems, leading to faster convergence. One of the keys\nto achieve acceleration in theory and in practice is to solve these\nsub-problems with appropriate accuracy by using the right stopping criterion\nand the right warm-start strategy. We give practical guidelines to use Catalyst\nand present a comprehensive analysis of its global complexity. We show that\nCatalyst applies to a large class of algorithms, including gradient descent,\nblock coordinate descent, incremental algorithms such as SAG, SAGA, SDCA, SVRG,\nMISO/Finito, and their proximal variants. For all of these methods, we\nestablish faster rates using the Catalyst acceleration, for strongly convex and\nnon-strongly convex objectives. We conclude with extensive experiments showing\nthat acceleration is useful in practice, especially for ill-conditioned\nproblems.","url_abs":"http://arxiv.org/abs/1712.05654v2","url_pdf":"http://arxiv.org/pdf/1712.05654v2.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":"catalyst-acceleration-for-first-order-convex","repo_url":"https://github.com/hongzhoulin89/Catalyst-QNing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"saga","method_name":"SAGA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.05654","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}