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It is proved that this method converges to the unique solution of the linear least\u2010squares problem when its coefficient matrix is of full rank, with the number of rows being no less than the number of columns. Numerical results show that the greedy randomized coordinate descent method is more efficient than the randomized coordinate descent method.<\/jats:p>","DOI":"10.1002\/nla.2237","type":"journal-article","created":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T11:41:26Z","timestamp":1553168486000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["On greedy randomized coordinate descent methods for solving large linear least\u2010squares problems"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8353-3803","authenticated-orcid":false,"given":"Zhong\u2010Zhi","family":"Bai","sequence":"first","affiliation":[{"name":"State Key Laboratory of Scientific\/Engineering Computing Institute of Computational Mathematics and Scientific\/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences  Beijing China"},{"name":"School of Mathematical Sciences 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