{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T12:43:40Z","timestamp":1769863420062,"version":"3.49.0"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006435","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS 1708906"],"award-info":[{"award-number":["ECCS 1708906"]}],"id":[{"id":"10.13039\/100006435","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006435","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS 1809833"],"award-info":[{"award-number":["ECCS 1809833"]}],"id":[{"id":"10.13039\/100006435","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Contr."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tac.2024.3453656","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T13:45:39Z","timestamp":1725371139000},"page":"889-904","source":"Crossref","is-referenced-by-count":4,"title":["Tradeoffs Between Convergence Rate and Noise Amplification for Momentum-Based Accelerated Optimization Algorithms"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-1536","authenticated-orcid":false,"given":"Hesameddin","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4342-6661","authenticated-orcid":false,"given":"Meisam","family":"Razaviyayn","sequence":"additional","affiliation":[{"name":"Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4181-2924","authenticated-orcid":false,"given":"Mihailo R.","family":"Jovanovi\u0107","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/0041-5553(64)90137-5"},{"key":"ref2","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate $O(1\/k^{2})$","volume-title":"Proc. Dokl. Akad. Nauk SSSR","volume":"27","author":"Nesterov","year":"1983"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-012-0629-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.538"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2015.2481563"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2019.8814459"},{"key":"ref7","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sutskever","year":"2013"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91578-4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/15M1009597"},{"key":"ref10","first-page":"1549","article-title":"Dissipativity theory for Nesterov\u2019s accelerated method","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Hu","year":"2017"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2018.8430824"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2017.2722406"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1137\/17M1136845"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CDC49753.2023.10384198"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-013-0653-0"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1137\/17M112124X"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1137\/16m108104x"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1137\/14095697X"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10589-021-00338-8"},{"issue":"1","key":"ref21","first-page":"6","article-title":"Comparison of the convergence rates for single-step and multi-step optimization algorithms in the presence of noise","volume":"15","author":"Polyak","year":"1977","journal-title":"Eng. Cybern."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015187"},{"key":"ref23","first-page":"2113","article-title":"Gradient-based hyperparameter optimization through reversible learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Maclaurin","year":"2015"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472612"},{"key":"ref25","first-page":"3458","article-title":"On optimal generalizability in parametric learning","volume-title":"Proc. Neural Inf. Process.","author":"Beirami","year":"2017"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/00207179.2020.1745286"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/BF02096261"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/070704277"},{"key":"ref30","article-title":"Exactness, inexactness and stochasticity in first-order methods for large-scale convex optimization","author":"Devolder","year":"2013"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-016-0999-6"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-013-9150-3"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1134\/S0005117919090066"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3162154"},{"key":"ref35","first-page":"891","article-title":"Accelerated linear convergence of stochastic momentum methods in Wasserstein distances","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Can","year":"2019"},{"key":"ref36","article-title":"Stochastic first order methods in smooth convex optimization","author":"Devolder","year":"2011"},{"key":"ref37","first-page":"1019","article-title":"On acceleration with noise-corrupted gradients","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Cohen","year":"2018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/110848876"},{"key":"ref39","article-title":"A universally optimal multistage accelerated stochastic gradient method","volume-title":"Proc. Neural Inf. Process.","author":"Aybat","year":"2019"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3008297"},{"key":"ref41","article-title":"The speed-robustness trade-off for first-order methods with additive gradient noise","author":"Van Scoy","year":"2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/19M1244925"},{"key":"ref43","article-title":"Entropic risk-averse generalized momentum methods","author":"Can","year":"2022"},{"key":"ref44","article-title":"Understanding the role of momentum in stochastic gradient methods","volume-title":"Proc. Neural Inf. Process.","volume":"32","author":"Gitman","year":"2019"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1137\/20M1355847"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9"},{"issue":"1","key":"ref47","first-page":"4303","article-title":"On lower and upper bounds in smooth and strongly convex optimization","volume":"17","author":"Arjevani","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00068"},{"key":"ref49","article-title":"Introduction to optimization","volume":"1","author":"Polyak","year":"1987","journal-title":"Optim. Softw. Inc., New York"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1137\/0329055"},{"key":"ref52","first-page":"1674","article-title":"Non-convex learning via stochastic gradient Langevin dynamics: A nonasymptotic analysis","volume-title":"Proc. Conf. Learn. Theory","author":"Raginsky","year":"2017"},{"key":"ref53","first-page":"1980","article-title":"A hitting time analysis of stochastic gradient Langevin dynamics","volume-title":"Proc. Conf. Learn. Theory","author":"Zhang","year":"2017"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2023.111129"},{"key":"ref55","volume-title":"Linear Optimal Control Systems","author":"Kwakernaak","year":"1972"},{"key":"ref56","volume-title":"Discrete-Time Control Systems","author":"Ogata","year":"1994"},{"key":"ref57","article-title":"Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms","volume-title":"arXiv:2209.11920","author":"Mohammadi","year":"2022"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1978.1101744"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2012.2202069"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619183"},{"key":"ref61","author":"Nemirovsky","year":"1983","journal-title":"Problem Complexity and Method Efficiency in Optimization"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/tac.2024.3438808"},{"key":"ref63","first-page":"1157","article-title":"A unified analysis of stochastic optimization methods using jump system theory and quadratic constraints","volume-title":"Proc. Conf. Learn. Theory","author":"Hu","year":"2017"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-020-01486-1"}],"container-title":["IEEE Transactions on Automatic Control"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/9\/10857662\/10663923-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9\/10857662\/10663923.pdf?arnumber=10663923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T21:06:52Z","timestamp":1769807212000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10663923\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":64,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tac.2024.3453656","relation":{},"ISSN":["0018-9286","1558-2523","2334-3303"],"issn-type":[{"value":"0018-9286","type":"print"},{"value":"1558-2523","type":"electronic"},{"value":"2334-3303","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}