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Two popular approaches for\nsolving the problem are nuclear-norm-regularized matrix approximation (Candes\nand Tao, 2009, Mazumder, Hastie and Tibshirani, 2010), and maximum-margin\nmatrix factorization (Srebro, Rennie and Jaakkola, 2005). These two procedures\nare in some cases solving equivalent problems, but with quite different\nalgorithms. In this article we bring the two approaches together, leading to an\nefficient algorithm for large matrix factorization and completion that\noutperforms both of these. We develop a software package \"softImpute\" in R for\nimplementing our approaches, and a distributed version for very large matrices\nusing the \"Spark\" cluster programming environment.","url_abs":"http://arxiv.org/abs/1410.2596v1","url_pdf":"http://arxiv.org/pdf/1410.2596v1.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":"matrix-completion-and-low-rank-svd-via-fast","repo_url":"https://github.com/hahmadraza/Handling-Missing-Data-using-ML-techniques","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"matrix-completion-and-low-rank-svd-via-fast","repo_url":"https://github.com/hammerlab/fancyimpute","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"matrix-completion-and-low-rank-svd-via-fast","repo_url":"https://github.com/iskandr/fancyimpute","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"matrix-completion-and-low-rank-svd-via-fast","repo_url":"https://github.com/oliviaxyl/A-Simple-Movie-Recommender-System-to-Classmates-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"matrix-completion-and-low-rank-svd-via-fast","repo_url":"https://github.com/travisbrady/py-soft-impute","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"matrix-completion","task_name":"Matrix Completion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1410.2596","atlas_url":"https://app.syntology.ai/?focus=1410.2596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1410.2596"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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