{"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/pyclustrpath-an-efficient-python-package-for","title":"PyClustrPath: An efficient Python package for generating clustering paths with GPU acceleration","arxiv_id":"2501.15964","date":"2025-01-27","proceeding":null,"authors":["Hongfei Wu","Yancheng Yuan"],"abstract":"Convex clustering is a popular clustering model without requiring the number of clusters as prior knowledge. It can generate a clustering path by continuously solving the model with a sequence of regularization parameter values. This paper introduces {\\it PyClustrPath}, a highly efficient Python package for solving the convex clustering model with GPU acceleration. {\\it PyClustrPath} implements popular first-order and second-order algorithms with a clean modular design. Such a design makes {\\it PyClustrPath} more scalable to incorporate new algorithms for solving the convex clustering model in the future. We extensively test the numerical performance of {\\it PyClustrPath} on popular clustering datasets, demonstrating its superior performance compared to the existing solvers for generating the clustering path based on the convex clustering model. The implementation of {\\it PyClustrPath} can be found at: https://github.com/D3IntOpt/PyClustrPath.","url_abs":"https://arxiv.org/abs/2501.15964v1","url_pdf":"https://arxiv.org/pdf/2501.15964v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"pyclustrpath-an-efficient-python-package-for","repo_url":"https://github.com/d3intopt/pyclustrpath","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}