{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:39:39Z","timestamp":1740145179577,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T00:00:00Z","timestamp":1578096000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T00:00:00Z","timestamp":1578096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1720237"],"award-info":[{"award-number":["DMS-1720237"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000141712162"],"award-info":[{"award-number":["N000141712162"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Optim Lett"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s11590-019-01520-y","type":"journal-article","created":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T11:03:04Z","timestamp":1578135784000},"page":"1583-1598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Linear convergence of cyclic SAGA"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0970-9214","authenticated-orcid":false,"given":"Youngsuk","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6820-9095","authenticated-orcid":false,"given":"Ernest K.","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,4]]},"reference":[{"issue":"5","key":"1520_CR1","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.1109\/TIT.2011.2182178","volume":"58","author":"A Agarwal","year":"2012","unstructured":"Agarwal, A., Bartlett, P.L., Ravikumar, P., Wainwright, M.J.: Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. IEEE Trans. Inf. Theory 58(5), 3235\u20133249 (2012)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"2","key":"1520_CR2","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10107-011-0472-0","volume":"129","author":"DP Bertsekas","year":"2011","unstructured":"Bertsekas, D.P.: Incremental proximal methods for large scale convex optimization. Math. Program. 129(2), 163\u2013195 (2011)","journal-title":"Math. Program."},{"key":"1520_CR3","unstructured":"Bertsekas, D.P.: Incremental aggregated proximal and augmented Lagrangian algorithms. Comput. Sci. Syst. Control. arXiv preprint arXiv:1509.09257 (2015)"},{"issue":"1","key":"1520_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1137\/040615961","volume":"18","author":"D Blatt","year":"2007","unstructured":"Blatt, D., Hero, A.O., Gauchman, H.: A convergent incremental gradient method with a constant step size. SIAM J. Optim. 18(1), 29\u201351 (2007)","journal-title":"SIAM J. Optim."},{"key":"1520_CR5","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1\u201327:27 (2011). Software available at http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm"},{"key":"1520_CR6","unstructured":"Defazio, A.: A simple practical accelerated method for finite sums. In: NIPS, pp. 676\u2013684 (2016)"},{"key":"1520_CR7","unstructured":"Defazio, A., Bach, F., Lacoste-Julien, A.: SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives. In: NIPS, pp. 1646\u20131654 (2014)"},{"key":"1520_CR8","first-page":"1125","volume":"32","author":"A Defazio","year":"2014","unstructured":"Defazio, A., Domke, J., Caetano, T.S.: Finito: a faster, permutable incremental gradient method for big data problems. ICML 32, 1125\u20131133 (2014)","journal-title":"ICML"},{"issue":"2","key":"1520_CR9","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1137\/15M1049695","volume":"27","author":"M G\u00fcrb\u00fczbalaban","year":"2017","unstructured":"G\u00fcrb\u00fczbalaban, M., Ozdaglar, A., Parrilo, P.A.: On the convergence rate of incremental aggregated gradient algorithms. SIAM J. Optim. 27(2), 1035\u20131048 (2017)","journal-title":"SIAM J. Optim."},{"key":"1520_CR10","unstructured":"Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. In: NIPS, pp. 315\u2013323 (2013)"},{"key":"1520_CR11","volume-title":"Stochastic Approximation and Recursive Algorithms and Applications","author":"HJ Kushner","year":"2003","unstructured":"Kushner, H.J., Yin, G.G.: Stochastic Approximation and Recursive Algorithms and Applications, 2nd edn. Springer, New York (2003)","edition":"2"},{"issue":"1","key":"1520_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s10107-017-1173-0","volume":"171","author":"G Lan","year":"2018","unstructured":"Lan, G., Zhou, Y.: An optimal randomized incremental gradient method. Math. Program. 171(1), 167\u2013215 (2018)","journal-title":"Math. Program."},{"key":"1520_CR13","unstructured":"Le Roux, N., Schmidt, M., Bach, F.: Stochastic gradient method with an exponential convergence rate for finite training sets. In: NIPS (2012)"},{"key":"1520_CR14","unstructured":"Mairal, J.: Optimization with first-order surrogate functions. In: ICML, pp. 783\u2013791 (2013)"},{"issue":"2","key":"1520_CR15","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1137\/140957639","volume":"25","author":"J Mairal","year":"2015","unstructured":"Mairal, J.: Incremental majorization\u2013minimization optimization with application to large-scale machine learning. SIAM J. Optim. 25(2), 829\u2013855 (2015)","journal-title":"SIAM J. Optim."},{"issue":"2","key":"1520_CR16","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1137\/16M1101702","volume":"28","author":"A Mokhtari","year":"2018","unstructured":"Mokhtari, A., Gurbuzbalaban, M., Ribeiro, A.: Surpassing gradient descent provably: Acyclic incremental method with linear convergence rate. SIAM J. Optim. 28(2), 1420\u20131447 (2018)","journal-title":"SIAM J. Optim."},{"key":"1520_CR17","volume-title":"Problem Complexity and Method Efficiency in Optimization","author":"AS Nemirovski","year":"1983","unstructured":"Nemirovski, A.S., Yudin, D.B.: Problem Complexity and Method Efficiency in Optimization. Wiley, New York (1983)"},{"key":"1520_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9","volume-title":"Introductory Lectures on Convex Optimization: A Basic Course","author":"Y Nesterov","year":"2004","unstructured":"Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course. Springer, New York (2004)"},{"key":"1520_CR19","unstructured":"Nitanda, A.: Stochastic proximal gradient descent with acceleration techniques. In: NIPS, pp. 1574\u20131582 (2014)"},{"key":"1520_CR20","volume-title":"Introduction to Optimization","author":"BT Polyak","year":"1987","unstructured":"Polyak, B.T.: Introduction to Optimization. Optimization Software Inc, New York (1987)"},{"key":"1520_CR21","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400\u2013407 (1951)","journal-title":"Ann. Math. Stat."},{"issue":"1\u20132","key":"1520_CR22","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s10107-016-1030-6","volume":"162","author":"M Schmidt","year":"2017","unstructured":"Schmidt, M., Le Roux, N., Bach, F.: Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1\u20132), 83\u2013112 (2017)","journal-title":"Math. Program."},{"key":"1520_CR23","unstructured":"Shalev-Shwartz, S.: SDCA without duality, regularization and individual convexity. In: ICML, pp. 747\u2013754 (2016)"},{"key":"1520_CR24","first-page":"567","volume":"14","author":"S Shalev-Shwartz","year":"2013","unstructured":"Shalev-Shwartz, S., Zhang, T.: Stochastic dual coordinate ascent methods for regularized loss. J. Mach. Learn. Res. 14, 567\u2013599 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"1520_CR25","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10107-014-0839-0","volume":"155","author":"S Shalev-Shwartz","year":"2016","unstructured":"Shalev-Shwartz, S., Zhang, T.: Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization. Math. Program. 155(1), 105\u2013145 (2016)","journal-title":"Math. Program."},{"key":"1520_CR26","first-page":"9","volume-title":"Notes of Scientific Seminar on Theory and Applications of Cybernetics and Operations Research","author":"NZ Shor","year":"1962","unstructured":"Shor, N.Z.: Notes of Scientific Seminar on Theory and Applications of Cybernetics and Operations Research, pp. 9\u201317. Ukrainian Academy of Sciences, Kiev (1962)"},{"key":"1520_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-82118-9","volume-title":"Minimization Methods for Non-differentiable Functions","author":"NZ Shor","year":"1985","unstructured":"Shor, N.Z., Kiwiel, K.C., Ruszcay\u0144ski, A.: Minimization Methods for Non-differentiable Functions. Springer, New York (1985)"},{"issue":"3","key":"1520_CR28","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1007\/s10957-013-0409-2","volume":"160","author":"P Tseng","year":"2014","unstructured":"Tseng, P., Yun, S.: Incrementally updated gradient methods for constrained and regularized optimization. J. Optim. Theory Appl. 160(3), 832\u2013853 (2014)","journal-title":"J. Optim. Theory Appl."},{"key":"1520_CR29","unstructured":"Vanli, N.D., G\u00fcrb\u00fczbalaban, M., Ozdaglar, A.: A simple proof for the iteration complexity of the proximal gradient algorithm. NIPS Workshop on Optimization for Machine Learning (2016)"},{"key":"1520_CR30","unstructured":"Vanli, N.D., G\u00fcrb\u00fczbalaban, M., Ozdaglar, A.: A stronger convergence result on the proximal incremental aggregated gradient method. arXiv (2016)"},{"issue":"2","key":"1520_CR31","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1137\/16M1094415","volume":"28","author":"ND Vanli","year":"2018","unstructured":"Vanli, N.D., G\u00fcrb\u00fczbalaban, M., Ozdaglar, A.: Global convergence rate of proximal incremental aggregated gradient methods. SIAM J. Optim. 28(2), 1282\u20131300 (2018)","journal-title":"SIAM J. Optim."},{"issue":"2","key":"1520_CR32","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s10107-014-0769-x","volume":"150","author":"M Wang","year":"2015","unstructured":"Wang, M., Bertsekas, D.P.: Incremental constraint projection methods for variational inequalities. Math. Program. 150(2), 321\u2013363 (2015)","journal-title":"Math. Program."},{"issue":"4","key":"1520_CR33","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1137\/140961791","volume":"24","author":"L Xiao","year":"2014","unstructured":"Xiao, L., Zhang, T.: A proximal stochastic gradient method with progressive variance reduction. SIAM J. Optim. 24(4), 2057\u20132075 (2014)","journal-title":"SIAM J. Optim."},{"key":"1520_CR34","doi-asserted-by":"crossref","unstructured":"Ying B., Yuan, K., Sayed A.H.: Variance-reduced stochastic learning under random reshuffling. arXiv preprint arXiv:1708.01383 (2017)","DOI":"10.1109\/ICASSP.2018.8461739"},{"key":"1520_CR35","unstructured":"Zhang, H., Dai, Y.H., Guo, L., Peng, W.: Proximal-like incremental aggregated gradient method with linear convergence under Bregman distance growth conditions. arXiv preprint arXiv:1711.01136 (2017)"},{"key":"1520_CR36","unstructured":"Zhang, L., Mahdavi, M., Jin, R.: Linear convergence with condition number independent access of full gradients. In: NIPS, pp. 980\u2013988 (2013)"}],"container-title":["Optimization Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11590-019-01520-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11590-019-01520-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11590-019-01520-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:32:47Z","timestamp":1609633967000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11590-019-01520-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,4]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["1520"],"URL":"https:\/\/doi.org\/10.1007\/s11590-019-01520-y","relation":{},"ISSN":["1862-4472","1862-4480"],"issn-type":[{"type":"print","value":"1862-4472"},{"type":"electronic","value":"1862-4480"}],"subject":[],"published":{"date-parts":[[2020,1,4]]},"assertion":[{"value":"26 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}