{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T04:15:46Z","timestamp":1727928946679},"reference-count":24,"publisher":"Walter de Gruyter GmbH","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12371378"],"award-info":[{"award-number":["12371378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J05034"],"award-info":[{"award-number":["2020J05034"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>A high-performance sparse model is very important for processing high-dimensional data. Therefore, based on the quadratic approximate greed pursuit (QAGP) method, we can make full use of the information of the quadratic lower bound of its approximate function to get the relaxation quadratic approximate greed pursuit (RQAGP) method. The calculation process of the RQAGP method is to construct two inexact quadratic approximation functions by using the <jats:italic>m<\/jats:italic>-strongly convex and <jats:italic>L<\/jats:italic>-smooth characteristics of the objective function and then solve the approximation function iteratively by using the Iterative\nHard Thresholding (IHT) method to get the solution of the problem. The convergence analysis is given, and the performance of the method in the sparse logistic regression model is verified on synthetic data and real data sets. The results show that the RQAGP method is effective.<\/jats:p>","DOI":"10.1515\/cmam-2023-0050","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T16:20:51Z","timestamp":1696350051000},"page":"909-920","source":"Crossref","is-referenced-by-count":0,"title":["Relaxation Quadratic Approximation Greedy Pursuit Method Based on Sparse Learning"],"prefix":"10.1515","volume":"24","author":[{"given":"Shihai","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics & FJKLMAA , Fujian Normal University , Fuzhou 350117 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics & FJKLMAA , Fujian Normal University ; and School of Big Data, Fuzhou University of International Studies and Trade , Fuzhou 350202 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"2024100217254067592_j_cmam-2023-0050_ref_001","unstructured":"N.  Agarwal, B.  Bullins and E.  Hazan,\nSecond-order stochastic optimization in linear time,\nStat 1050 (2016), Paper No. 15."},{"key":"2024100217254067592_j_cmam-2023-0050_ref_002","unstructured":"Y.  Arjevani, S.  Shalev-Shwartz and O.  Shamir,\nOn lower and upper bounds in smooth and strongly convex optimization,\nJ. Mach. Learn. Res. 17 (2016), 4303\u20134353."},{"key":"2024100217254067592_j_cmam-2023-0050_ref_003","unstructured":"S.  Bahmani, B.  Raj and P. T.  Boufounos,\nGreedy sparsity-constrained optimization,\nJ. Mach. Learn. Res. 14 (2013), 807\u2013841."},{"key":"2024100217254067592_j_cmam-2023-0050_ref_004","unstructured":"C. M.  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