{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:55:44Z","timestamp":1771520144784,"version":"3.50.1"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS\u00a01708906"],"award-info":[{"award-number":["ECCS\u00a01708906"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ECCS\u00a01809833"],"award-info":[{"award-number":["ECCS\u00a01809833"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-16-1-0009"],"award-info":[{"award-number":["FA9550-16-1-0009"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Contr."],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1109\/tac.2020.3008297","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T20:07:26Z","timestamp":1594325246000},"page":"2480-2495","source":"Crossref","is-referenced-by-count":27,"title":["Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems"],"prefix":"10.1109","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-1536","authenticated-orcid":false,"given":"Hesameddin","family":"Mohammadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4342-6661","authenticated-orcid":false,"given":"Meisam","family":"Razaviyayn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4181-2924","authenticated-orcid":false,"given":"Mihailo R.","family":"Jovanovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2012.2202052"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2006.08.010"},{"key":"ref33","article-title":"Estimate sequence methods: Extensions and approximations","author":"baes","year":"2009"},{"key":"ref32","first-page":"650","article-title":"Averaging stochastic gradient descent on Riemannian manifolds","author":"tripuraneni","year":"0","journal-title":"Proc 31st Conf Learn Theory"},{"key":"ref31","first-page":"451","article-title":"Non-asymptotic analysis of stochastic approximation algorithms for machine learning","author":"moulines","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref30","first-page":"3520","article-title":"Harder, better, faster, stronger convergence rates for least-squares regression","volume":"18","author":"dieuleveut","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref37","author":"kwakernaak","year":"1972","journal-title":"Linear Optimal Control Systems"},{"key":"ref36","volume":"1","author":"polyak","year":"1987","journal-title":"Introduction to Optimization"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/140994964"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1137\/060676386"},{"key":"ref28","first-page":"98","article-title":"New stochastic approximation type procedures","volume":"7","author":"polyak","year":"1990","journal-title":"Autom i Telemekh"},{"key":"ref27","first-page":"595","article-title":"Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression","volume":"15","author":"bach","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/0330046"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.538"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref22","article-title":"Exactness, inexactness and stochasticity in first-order methods for large-scale convex optimization","author":"devolder","year":"2013"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/070704277"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-016-0999-6"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-013-0677-5"},{"key":"ref26","article-title":"Stochastic first order methods in smooth convex optimization","author":"devolder","year":"2011"},{"key":"ref25","first-page":"1458","article-title":"Convergence rates of inexact proximal-gradient methods for convex optimization","author":"schmidt","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1137\/15M1009597"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/9.587335"},{"key":"ref58","article-title":"Robustness of accelerated first-order algorithms for strongly convex optimization problems","author":"mohammadi","year":"2019"},{"key":"ref57","author":"bertsekas","year":"2015","journal-title":"Convex optimization algorithms"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2017.08.1513"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2867589"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2018.8430824"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1137\/17M1136845"},{"key":"ref52","first-page":"1549","article-title":"Dissipativity theory for Nesterov's accelerated method","volume":"70","author":"hu","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref10","first-page":"2113","article-title":"Gradient-based hyperparameter optimization through reversible learning","author":"maclaurin","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015187"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2011.2181790"},{"key":"ref12","first-page":"3458","article-title":"On optimal generalizability in parametric learning","author":"beirami","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref13","first-page":"1467","article-title":"Global convergence of policy gradient methods for the linear quadratic regulator","author":"fazel","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","article-title":"Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem","author":"mohammadi","year":"0","journal-title":"IEEE Trans Autom Control"},{"key":"ref15","first-page":"1","article-title":"Learning the model-free linear quadratic regulator via random search","volume":"120","author":"mohammadi","year":"0","journal-title":"Proc 2nd Annu Conf Learn Dyn Control"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2020.3006256"},{"key":"ref17","first-page":"797","article-title":"Escaping from saddle points&#x2014;Online stochastic gradient for tensor decomposition","volume":"40","author":"ge","year":"0","journal-title":"Proc 28th Conf Learn Theory"},{"key":"ref18","first-page":"1724","article-title":"How to escape saddle points efficiently","volume":"70","author":"jin","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/BF02096261"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2015.2481563"},{"key":"ref6","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"sutskever","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-012-0629-5"},{"key":"ref8","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate $O(1\/k^2)$","volume":"27","author":"nesterov","year":"0","journal-title":"Proc Dokl Akad Nauk SSSR"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/0041-5553(64)90137-5"},{"key":"ref49","article-title":"Robust and structure exploiting optimization algorithms: An integral quadratic constraint approach","author":"michalowsky","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91578-4"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1137\/19M1244925"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2019.8814680"},{"key":"ref48","first-page":"6602","article-title":"On the influence of momentum acceleration on online learning","volume":"17","author":"yuan","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref47","first-page":"8525","article-title":"A universally optimal multistage accelerated stochastic gradient method","author":"aybat","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2016.7852233"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2014.2304465"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619183"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7798532"}],"container-title":["IEEE Transactions on Automatic Control"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/9\/9442408\/9137636-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9\/9442408\/09137636.pdf?arnumber=9137636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:07Z","timestamp":1652194387000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9137636\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":58,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tac.2020.3008297","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":[[2021,6]]}}}