{"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-hybrid-first-order-method-for-nonconvex-ell","title":"Minimization Over the Nonconvex Sparsity Constraint Using A Hybrid First-order method","arxiv_id":"2104.04400","date":"2021-04-09","proceeding":null,"authors":["Xiangyu Yang","Hao Wang","Yichen Zhu","Xiao Wang"],"abstract":"We investigate a class of nonconvex optimization problems characterized by a feasible set consisting of level-bounded nonconvex regularizers, with a continuously differentiable objective. We propose a novel hybrid approach to tackle such structured problems within a first-order algorithmic framework by combining the Frank-Wolfe method and the gradient projection method. The Frank-Wolfe step is amenable to a closed-form solution, while the gradient projection step can be efficiently performed in a reduced subspace. A notable characteristic of our approach lies in its independence from introducing smoothing parameters, enabling efficient solutions to the original nonsmooth problems. We establish the global convergence of the proposed algorithm and show the $O(1/\\sqrt{k})$ convergence rate in terms of the optimality error for nonconvex objectives under reasonable assumptions. Numerical experiments underscore the practicality and efficiency of our proposed algorithm compared to existing cutting-edge methods. Furthermore, we highlight how the proposed algorithm contributes to the advancement of nonconvex regularizer-constrained optimization.","url_abs":"https://arxiv.org/abs/2104.04400v2","url_pdf":"https://arxiv.org/pdf/2104.04400v2.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":"a-hybrid-first-order-method-for-nonconvex-ell","repo_url":"https://github.com/optimizater/hybrid-1st-order-algorithm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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}