{"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/communication-efficient-distributed-svd-via","title":"Communication-Efficient Distributed SVD via Local Power Iterations","arxiv_id":"2002.08014","date":"2020-02-19","proceeding":null,"authors":["Xiang Li","Shusen Wang","Kun Chen","Zhihua Zhang"],"abstract":"We study distributed computing of the truncated singular value decomposition problem. We develop an algorithm that we call \\texttt{LocalPower} for improving communication efficiency. Specifically, we uniformly partition the dataset among $m$ nodes and alternate between multiple (precisely $p$) local power iterations and one global aggregation. In the aggregation, we propose to weight each local eigenvector matrix with orthogonal Procrustes transformation (OPT). As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\\pm 1$ entries as weights, has better computation complexity and stability in experiments. We theoretically show that under certain assumptions \\texttt{LocalPower} lowers the required number of communications by a factor of $p$ to reach a constant accuracy. We also show that the strategy of periodically decaying $p$ helps obtain high-precision solutions. We conduct experiments to demonstrate the effectiveness of \\texttt{LocalPower}.","url_abs":"https://arxiv.org/abs/2002.08014v4","url_pdf":"https://arxiv.org/pdf/2002.08014v4.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":"communication-efficient-distributed-svd-via","repo_url":"https://github.com/lx10077/LocalPower","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"distributed-computing","task_name":"Distributed Computing"}],"methods":[{"method_slug":"procrustes","method_name":"Procrustes"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}