{"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/sparse-estimation-with-the-swept-approximated","title":"Sparse Estimation with the Swept Approximated Message-Passing Algorithm","arxiv_id":"1406.4311","date":"2014-06-17","proceeding":null,"authors":["Andre Manoel","Florent Krzakala","Eric W. Tramel","Lenka Zdeborová"],"abstract":"Approximate Message Passing (AMP) has been shown to be a superior method for\ninference problems, such as the recovery of signals from sets of noisy,\nlower-dimensionality measurements, both in terms of reconstruction accuracy and\nin computational efficiency. However, AMP suffers from serious convergence\nissues in contexts that do not exactly match its assumptions. We propose a new\napproach to stabilizing AMP in these contexts by applying AMP updates to\nindividual coefficients rather than in parallel. Our results show that this\nchange to the AMP iteration can provide theoretically expected, but hitherto\nunobtainable, performance for problems on which the standard AMP iteration\ndiverges. Additionally, we find that the computational costs of this swept\ncoefficient update scheme is not unduly burdensome, allowing it to be applied\nefficiently to signals of large dimensionality.","url_abs":"http://arxiv.org/abs/1406.4311v1","url_pdf":"http://arxiv.org/pdf/1406.4311v1.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":"sparse-estimation-with-the-swept-approximated","repo_url":"https://github.com/eric-tramel/SwAMP-Demo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}