{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:59:00Z","timestamp":1753883940136,"version":"3.41.2"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12071022"],"award-info":[{"award-number":["12071022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Natural Science Foundation","award":["Z190002"],"award-info":[{"award-number":["Z190002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Asia Pac. J. Oper. Res."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss\u2013Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.<\/jats:p>","DOI":"10.1142\/s0217595922500014","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T02:53:03Z","timestamp":1636253583000},"source":"Crossref","is-referenced-by-count":0,"title":["Fast Algorithms for LS and LAD-Collaborative Regression"],"prefix":"10.1142","volume":"39","author":[{"given":"Jun","family":"Sun","sequence":"first","affiliation":[{"name":"School of Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingchen","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. 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