{"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/a-fast-and-flexible-algorithm-for-the-graph","title":"A Fast and Flexible Algorithm for the Graph-Fused Lasso","arxiv_id":"1505.06475","date":"2015-05-24","proceeding":null,"authors":["Wesley Tansey","James G. Scott"],"abstract":"We propose a new algorithm for solving the graph-fused lasso (GFL), a method\nfor parameter estimation that operates under the assumption that the signal\ntends to be locally constant over a predefined graph structure. Our key insight\nis to decompose the graph into a set of trails which can then each be solved\nefficiently using techniques for the ordinary (1D) fused lasso. We leverage\nthese trails in a proximal algorithm that alternates between closed form primal\nupdates and fast dual trail updates. The resulting techinque is both faster\nthan previous GFL methods and more flexible in the choice of loss function and\ngraph structure. Furthermore, we present two algorithms for constructing trail\nsets and show empirically that they offer a tradeoff between preprocessing time\nand convergence rate.","url_abs":"http://arxiv.org/abs/1505.06475v3","url_pdf":"http://arxiv.org/pdf/1505.06475v3.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":"a-fast-and-flexible-algorithm-for-the-graph","repo_url":"https://github.com/yaglm/yaglm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}