{"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-reference-searching-based-algorithm-for","title":"A reference-searching-based algorithm for large-scale data envelopment analysis computation","arxiv_id":"1710.10482","date":"2017-10-28","proceeding":null,"authors":["Wen-Chih Chen"],"abstract":"Data envelopment analysis (DEA) is a linear program (LP)-based method used to determine the efficiency of a decision making unit (DMU), which transforms inputs to outputs, by peer comparison. This paper presents a new computation algorithm to determine DEA efficiencies by solving small-size LPs instead of a full-size LP. The concept is based on searching the corresponding references, which is a subset of the efficient DMUs with numbers no greater than the dimension (number of inputs and outputs). The results of empirical case studies show that the proposed algorithm computes 3 times faster than the current state of the art for large-scale, high-dimension, and high-density (percentage of efficient DMUs) cases. It is flexible enough to compute the efficiencies of a subset of the full data set without setup costs, and it can also serve as a sub-procedure for other algorithms.","url_abs":"http://arxiv.org/abs/1710.10482v2","url_pdf":"http://arxiv.org/pdf/1710.10482v2.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-reference-searching-based-algorithm-for","repo_url":"https://github.com/wen-chih/SearchRef.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}