{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T12:20:50Z","timestamp":1730204450125,"version":"3.28.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/cdc40024.2019.9029852","type":"proceedings-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T04:43:11Z","timestamp":1584074591000},"page":"2745-2751","source":"Crossref","is-referenced-by-count":2,"title":["Distributed Online Learning over Time-varying Graphs via Proximal Gradient Descent"],"prefix":"10.1109","author":[{"given":"Rishabh","family":"Dixit","sequence":"first","affiliation":[]},{"given":"Amrit Singh","family":"Bedi","sequence":"additional","affiliation":[]},{"given":"Ketan","family":"Rajawat","sequence":"additional","affiliation":[]},{"given":"Alec","family":"Koppel","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511608759"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.laa.2018.05.017"},{"article-title":"Technical report for online learning over time-varying graphs via proximal gradient descent","year":"0","author":"rishabh","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2009515"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/BF02124750"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2695450"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2298140"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1976.10286"},{"key":"ref13","first-page":"928","article-title":"Online convex programming and generalized infinitesimal gradient ascent","author":"zinkevich","year":"2003","journal-title":"Proc of ICML"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2015.2404790"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2015.1408"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7799379"},{"key":"ref17","first-page":"732","article-title":"Improved dynamic regret for non-degenerate functions","author":"zhang","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2797423"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2295055"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1214\/15-STS530"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-060117-105131"},{"journal-title":"Proximal gradient algorithms Applications in signal processing","year":"2018","author":"antonello","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2017.8335425"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2222398"},{"key":"ref29","first-page":"449","article-title":"Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient","volume":"48","author":"yang","year":"2016","journal-title":"Proc of ICML"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2351991"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2009.2028959"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3182\/20110828-6-IT-1002.01136"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2013.2248175"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2217\/iim.12.32"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2010.2043038"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CAMSAP.2015.7383847"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2743462"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2743462"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2012.6483273"},{"journal-title":"Online learning with inexact proximal online gradient descent algorithms","year":"2018","author":"dixit","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2364096"},{"key":"ref25","first-page":"1458","article-title":"Convergence rates of inexact proximal-gradient methods for convex optimization","author":"schmidt","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"}],"event":{"name":"2019 IEEE 58th Conference on Decision and Control (CDC)","start":{"date-parts":[[2019,12,11]]},"location":"Nice, France","end":{"date-parts":[[2019,12,13]]}},"container-title":["2019 IEEE 58th Conference on Decision and Control (CDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8977134\/9028853\/09029852.pdf?arnumber=9029852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T20:20:53Z","timestamp":1658262053000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9029852\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/cdc40024.2019.9029852","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}