{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:56:22Z","timestamp":1770544582223,"version":"3.49.0"},"reference-count":35,"publisher":"Informa UK Limited","issue":"2","funder":[{"DOI":"10.13039\/501100006595","name":"UEFISCDI","doi-asserted-by":"publisher","award":["NO Grants 2014\u20132021 RO-NO-2019-0184, under project ELO-Hyp, contract no. 24\/2020"],"award-info":[{"award-number":["NO Grants 2014\u20132021 RO-NO-2019-0184, under project ELO-Hyp, contract no. 24\/2020"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Optimization Methods and Software"],"published-print":{"date-parts":[[2024,3,3]]},"DOI":"10.1080\/10556788.2023.2256447","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T12:00:19Z","timestamp":1695643219000},"page":"384-413","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":3,"title":["Convergence analysis of stochastic higher-order majorization\u2013minimization algorithms"],"prefix":"10.1080","volume":"39","author":[{"given":"Daniela","family":"Lupu","sequence":"first","affiliation":[{"name":"Automatic Control and Systems Engineering Department, University Politehnica Bucharest, Bucharest, Romania"}]},{"given":"Ion","family":"Necoara","sequence":"additional","affiliation":[{"name":"Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Bucharest, Romania"}]}],"member":"301","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"A. Agafonov D. Kamzolov P. Dvurechensky and A. Gasnikov Inexact tensor methods and their application to stochastic convex optimization arXiv preprint: 2012.15636 2020."},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-016-1065-8"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/imanum\/dry009"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/050644641"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/10556788.2019.1678033"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_3_10_1","first-page":"1646","article-title":"SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives","volume":"27","author":"Defazio A.","year":"2014","unstructured":"A. Defazio, F. Bach, and S. Lacoste-Julien, SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives, Adv. Neural. Inf. Process. Syst. 27 (2014), pp. 1646\u20131654.","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-020-01606-x"},{"key":"e_1_3_3_12_1","unstructured":"N. Doikov and Y.U. Nesterov Inexact tensor methods with dynamic accuracies in International Conference on Machine Learning 2020 pp. 2577\u20132586."},{"key":"e_1_3_3_13_1","unstructured":"A. Gasnikov P. Dvurechensky E. Gorbunov E. Vorontsova D. Selikhanovych C.A. Uribe B. Jiang H. Wang S. Zhang S. Bubeck and Q. Jiang Near optimal methods for minimizing convex functions with Lipschitz pth derivatives In Conference on Learning Theory 2019 pp. 1392\u20131393."},{"key":"e_1_3_3_14_1","volume-title":"Deep Learning","author":"Goodfellow I.","year":"2016","unstructured":"I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016."},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471221317"},{"key":"e_1_3_3_16_1","unstructured":"R. Johnson and T. Zhang Accelerating stochastic gradient descent using predictive variance reduction in Advances in Neural Information Processing Systems pp. 315\u2013323 2013."},{"key":"e_1_3_3_17_1","unstructured":"D. Kovalev K. Mishchenko and P. Richtarik Stochastic Newton and cubic Newton methods with simple local linear-quadratic rates in Advances in Neural Information Processing Systems 2019."},{"key":"e_1_3_3_18_1","unstructured":"A. Lucchi and J. Kohler A Stochastic tensor method for non-convex optimization arXiv preprint: 1911.10367 2019."},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/17M1122943"},{"key":"e_1_3_3_20_1","first-page":"451","article-title":"Non-asymptotic analysis of stochastic approximation algorithms for machine learning","volume":"24","author":"Moulines E.","year":"2011","unstructured":"E. Moulines and F. Bach, Non-asymptotic analysis of stochastic approximation algorithms for machine learning, Adv. Neural Inform. Process. Syst. 24 (2011), pp. 451\u2013459.","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1137\/140957639"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-019-01449-1"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/10556788.2020.1854252"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-012-0629-5"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-021-01821-2"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2010.12.010"},{"key":"e_1_3_3_27_1","unstructured":"I. Necoara and D. Lupu General higher-order majorization\u2013minimization algorithms for (non)convex optimization arXiv preprint: 2010.13893 2020."},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1137\/070704277"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-006-0706-8"},{"key":"e_1_3_3_30_1","unstructured":"L.M. Nguyen J. Liu K. Scheinberg and M. Takac SARAH: A novel method for machine learning problems using stochastic recursive gradient in International Conference on Machine Learning 2017."},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-020-01653-6"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00245-019-09617-7"},{"key":"e_1_3_3_33_1","first-page":"2899","article-title":"Stochastic cubic regularization for fast nonconvex optimization","volume":"31","author":"Tripuraneni N.","year":"2018","unstructured":"N. Tripuraneni, M. Stern, C. Jin, J. Regier, and M.I. Jordan, Stochastic cubic regularization for fast nonconvex optimization, Adv. Neural. Inf. Process. Syst. 31 (2018), pp. 2899\u20132908.","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"e_1_3_3_34_1","unstructured":"D. Zhou P. Xu and Q. Gu Stochastic variance-reduced cubic regularized Newton method in International Conference on Machine Learning 2018 pp. 5985\u20135994."},{"key":"e_1_3_3_35_1","unstructured":"http:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral-Remote-Sensing-Scenes."},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-004-0559-y"}],"container-title":["Optimization Methods and Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/10556788.2023.2256447","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T15:14:01Z","timestamp":1727190841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/10556788.2023.2256447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3,3]]}},"alternative-id":["10.1080\/10556788.2023.2256447"],"URL":"https:\/\/doi.org\/10.1080\/10556788.2023.2256447","relation":{},"ISSN":["1055-6788","1029-4937"],"issn-type":[{"value":"1055-6788","type":"print"},{"value":"1029-4937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=goms20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=goms20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2022-05-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}