{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:54:24Z","timestamp":1776131664639,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2532"],"award-info":[{"award-number":["N00014-21-1-2532"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Optim Appl"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10589-023-00483-2","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T04:03:16Z","timestamp":1681876996000},"page":"79-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction"],"prefix":"10.1007","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2371-9398","authenticated-orcid":false,"given":"Albert S.","family":"Berahas","sequence":"first","affiliation":[]},{"given":"Jiahao","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Zihong","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Baoyu","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"483_CR1","unstructured":"Achiam, J., Held, D., Tamar, A., Abbeel, P.: Constrained policy optimization. In: international conference on machine learning, pp. 22\u201331 (2017). PMLR"},{"key":"483_CR2","doi-asserted-by":"crossref","unstructured":"Bai, J., Hager, W.W., Zhang, H.: An inexact accelerated stochastic ADMM for separable convex optimization. Comput. Optim. Appl. 81(2), 479\u2013518 (2022)","DOI":"10.1007\/s10589-021-00338-8"},{"key":"483_CR3","doi-asserted-by":"crossref","unstructured":"Berahas, A.S., Curtis, F.E., O\u2019Neill, M.J., Robinson, D.P.: A stochastic sequential quadratic optimization algorithm for nonlinear equality constrained optimization with rank-deficient Jacobians. arXiv preprint arXiv:2106.13015 (2021a)","DOI":"10.1137\/20M1354556"},{"issue":"2","key":"483_CR4","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1137\/20M1354556","volume":"31","author":"AS Berahas","year":"2021","unstructured":"Berahas, A.S., Curtis, F.E., Robinson, D., Zhou, B.: Sequential quadratic optimization for nonlinear equality constrained stochastic optimization. SIAM J. Optim. 31(2), 1352\u20131379 (2021)","journal-title":"SIAM J. Optim."},{"issue":"7","key":"483_CR5","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ac0966","volume":"37","author":"F Bian","year":"2021","unstructured":"Bian, F., Liang, J., Zhang, X.: A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization. Inverse Probl. 37(7), 075009 (2021)","journal-title":"Inverse Probl."},{"issue":"2","key":"483_CR6","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1137\/16M1080173","volume":"60","author":"L Bottou","year":"2018","unstructured":"Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. Siam Rev. 60(2), 223\u2013311 (2018)","journal-title":"Siam Rev."},{"issue":"1","key":"483_CR7","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1137\/060674004","volume":"19","author":"RH Byrd","year":"2008","unstructured":"Byrd, R.H., Curtis, F.E., Nocedal, J.: An inexact SQP method for equality constrained optimization. SIAM J. Optim. 19(1), 351\u2013369 (2008)","journal-title":"SIAM J. Optim."},{"issue":"3","key":"483_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1\u201327 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"513","key":"483_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1080\/01621459.2015.1123157","volume":"111","author":"N Chatterjee","year":"2016","unstructured":"Chatterjee, N., Chen, Y.-H., Maas, P., Carroll, R.J.: Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources. J. Am. Statistical Assoc. 111(513), 107\u2013117 (2016)","journal-title":"J. Am. Statistical Assoc."},{"key":"483_CR10","doi-asserted-by":"crossref","unstructured":"Chen, C., Tung, F., Vedula, N., Mori, G.: Constraint-aware deep neural network compression. In: proceedings of the European conference on computer vision (ECCV), pp. 400\u2013415 (2018)","DOI":"10.1007\/978-3-030-01237-3_25"},{"key":"483_CR11","unstructured":"Curtis, F.E., O\u2019Neill, M.J., Robinson, D.P.: Worst-Case Complexity of an SQP method for nonlinear equality constrained stochastic optimization. arXiv preprint arXiv:2112.14799 (2021a)"},{"key":"483_CR12","unstructured":"Curtis, F.E., Robinson, D.P., Zhou, B.: Inexact sequential quadratic optimization for minimizing a stochastic objective function subject to deterministic nonlinear equality constraints. arXiv preprint arXiv:2107.03512 (2021b)"},{"key":"483_CR13","unstructured":"Defazio, A., Bach, F., Lacoste-Julien, S.: SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Adv. Neural Inf. Process. Syst., pp. 1646\u20131654 (2014)"},{"issue":"415","key":"483_CR14","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1080\/01621459.1991.10475100","volume":"86","author":"CJ Geyer","year":"1991","unstructured":"Geyer, C.J.: Constrained maximum likelihood exemplified by isotonic convex logistic regression. J. Am. Statistical Assoc. 86(415), 717\u2013724 (1991)","journal-title":"J. Am. Statistical Assoc."},{"issue":"1\u20132","key":"483_CR15","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s10107-014-0846-1","volume":"155","author":"S Ghadimi","year":"2016","unstructured":"Ghadimi, S., Lan, G., Zhang, H.: Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization. Math. Program. 155(1\u20132), 267\u2013305 (2016)","journal-title":"Math. Program."},{"key":"483_CR16","first-page":"315","volume":"26","author":"R Johnson","year":"2013","unstructured":"Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction. Adv. Neural Inf. Process. Syst. 26, 315\u2013323 (2013)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"483_CR17","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s10107-010-0434-y","volume":"133","author":"G Lan","year":"2012","unstructured":"Lan, G.: An optimal method for stochastic composite optimization. Math. Program. 133(1), 365\u2013397 (2012)","journal-title":"Math. Program."},{"key":"483_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-39568-1","volume-title":"First-order and Stochastic Optimization Methods for Machine Learning","author":"G Lan","year":"2020","unstructured":"Lan, G.: First-order and Stochastic Optimization Methods for Machine Learning. Springer, Berlin (2020)"},{"key":"483_CR19","doi-asserted-by":"crossref","unstructured":"Lioutikov, R., Paraschos, A., Peters, J., Neumann, G.: Sample-based informationl-theoretic stochastic optimal control. In: 2014 IEEE international conference on robotics and automation (ICRA), pp. 3896\u20133902 (2014). IEEE","DOI":"10.1109\/ICRA.2014.6907424"},{"key":"483_CR20","doi-asserted-by":"crossref","unstructured":"Malikopoulos, A.A.: Stochastic optimal control for series hybrid electric vehicles. In: 2013 American control conference, pp. 1189\u20131194 (2013). IEEE","DOI":"10.1109\/ACC.2013.6579997"},{"key":"483_CR21","unstructured":"M\u00e1rquez-Neila, P., Salzmann, M., Fua, P.: Imposing hard constraints on deep networks: Promises and limitations. arXiv: 1706.02025 (2017)"},{"key":"483_CR22","doi-asserted-by":"crossref","unstructured":"Na, S., Anitescu, M., Kolar, M.: An adaptive stochastic sequential quadratic programming with differentiable exact augmented Lagrangians. arXiv preprint arXiv:2102.05320 (2021a)","DOI":"10.1007\/s10107-022-01846-z"},{"key":"483_CR23","unstructured":"Na, S., Anitescu, M., Kolar, M.: Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming. arXiv preprint arXiv:2109.11502 (2021b)"},{"key":"483_CR24","unstructured":"Nandwani, Y., Pathak, A., Singla, P.: A primal-dual formulation for deep learning with constraints. In: proceedings of neural information processing systems (NeurIPS), pp. 12157\u201312168 (2019)"},{"key":"483_CR25","unstructured":"N\u00e9giar, G., Dresdner, G., Tsai, A., El\u00a0Ghaoui, L., Locatello, F., Freund, R., Pedregosa, F.: Stochastic frank-wolfe for constrained finite-sum minimization. In: international conference on machine learning, pp. 7253\u20137262 (2020). PMLR"},{"issue":"4","key":"483_CR26","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1137\/070704277","volume":"19","author":"A Nemirovski","year":"2009","unstructured":"Nemirovski, A., Juditsky, A., Lan, G., Shapiro, A.: Robust stochastic approximation approach to stochastic programming. SIAM J. Optim. 19(4), 1574\u20131609 (2009)","journal-title":"SIAM J. Optim."},{"key":"483_CR27","unstructured":"Nguyen, L.M., Liu, J., Scheinberg, K., Tak\u00e1\u010d, M.: SARAH: A novel method for machine learning problems using stochastic recursive gradient. In: international conference on machine learning, pp. 2613\u20132621 (2017)"},{"key":"483_CR28","series-title":"Springer Series in Operations Research and Financial Engineering","volume-title":"Numerical Optimization","author":"J Nocedal","year":"2006","unstructured":"Nocedal, J., Wright, S.: Numerical Optimization. Springer Series in Operations Research and Financial Engineering, Springer, New York (2006)"},{"key":"483_CR29","unstructured":"Ouyang, H., He, N., Tran, L., Gray, A.: Stochastic alternating direction method of multipliers. In: international conference on machine learning, pp. 80\u201388 (2013). PMLR"},{"key":"483_CR30","doi-asserted-by":"crossref","unstructured":"Ravi, S.N., Dinh, T., Lokhande, V.S., Singh, V.: Explicitly imposing constraints in deep networks via conditional gradients gives improved generalization and faster convergence. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 4772\u20134779 (2019)","DOI":"10.1609\/aaai.v33i01.33014772"},{"key":"483_CR31","doi-asserted-by":"crossref","unstructured":"Reddi, S.J., Sra, S., P\u00f3czos, B., Smola, A.: Stochastic Frank-Wolfe methods for nonconvex optimization. In: 2016 54th annual Allerton conference on communication, control, and computing (Allerton), pp. 1244\u20131251 (2016a). IEEE","DOI":"10.1109\/ALLERTON.2016.7852377"},{"key":"483_CR32","doi-asserted-by":"crossref","unstructured":"Reddi, S.J., Hefny, A., Sra, S., Poczos, B., Smola, A.: Stochastic variance reduction for nonconvex optimization. In: international conference on machine learning, pp. 314\u2013323 (2016b). PMLR","DOI":"10.1109\/ALLERTON.2016.7852377"},{"key":"483_CR33","doi-asserted-by":"crossref","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann Math Statistics, 400\u2013407 (1951)","DOI":"10.1214\/aoms\/1177729586"},{"key":"483_CR34","volume-title":"Simulation","author":"SM Ross","year":"2013","unstructured":"Ross, S.M.: Simulation. Academic Press, Amsterdam (2013)"},{"key":"483_CR35","doi-asserted-by":"crossref","unstructured":"Roy, S.K., Mhammedi, Z., Harandi, M.: Geometry aware constrained optimization techniques for deep learning. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4460\u20134469 (2018)","DOI":"10.1109\/CVPR.2018.00469"},{"issue":"1\u20132","key":"483_CR36","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."},{"issue":"2","key":"483_CR37","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 minimization. J. Mach. Learn. Res. 14(2), 567 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"483_CR38","doi-asserted-by":"crossref","unstructured":"Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on stochastic programming: modeling and theory. SIAM, (2021)","DOI":"10.1137\/1.9781611976595"},{"key":"483_CR39","doi-asserted-by":"crossref","unstructured":"Shi, J., Spall, J.C.: SQP-based Projection SPSA algorithm for stochastic optimization with inequality constraints. In: 2021 American control conference (ACC), pp. 1244\u20131249 (2021). IEEE","DOI":"10.23919\/ACC50511.2021.9483007"},{"key":"483_CR40","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.ijepes.2015.02.024","volume":"72","author":"T Summers","year":"2015","unstructured":"Summers, T., Warrington, J., Morari, M., Lygeros, J.: Stochastic optimal power flow based on conditional value at risk and distributional robustness. Int. J. Electric. Power Energy Syst. 72, 116\u2013125 (2015)","journal-title":"Int. J. Electric. Power Energy Syst."},{"key":"483_CR41","volume-title":"Stochastic Optimization: Algorithms and Applications","author":"S Uryasev","year":"2013","unstructured":"Uryasev, S., Pardalos, P.M.: Stochastic Optimization: Algorithms and Applications, vol. 54. Springer (2013)"},{"key":"483_CR42","doi-asserted-by":"crossref","unstructured":"Vrakopoulou, M., Mathieu, J.L., Andersson, G.: Stochastic optimal power flow with uncertain reserves from demand response. In: 2014 47th Hawaii international conference on system sciences, pp. 2353\u20132362 (2014). IEEE","DOI":"10.1109\/HICSS.2014.296"},{"key":"483_CR43","volume-title":"Power Generation, Operation, and Control","author":"AJ Wood","year":"2013","unstructured":"Wood, A.J., Wollenberg, B.F., Shebl\u00e9, G.B.: Power Generation, Operation, and Control. Wiley, New Jersey, USA (2013)"},{"key":"483_CR44","unstructured":"Zhong, W., Kwok, J.: Fast stochastic alternating direction method of multipliers. In: international conference on machine learning, pp. 46\u201354 (2014). PMLR"},{"key":"483_CR45","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jcp.2019.05.024","volume":"394","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., Zabaras, N., Koutsourelakis, P.-S., Perdikaris, P.: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394, 56\u201381 (2019)","journal-title":"J. Comput. Phys."},{"key":"483_CR46","volume-title":"Stochastic Optimization Models in Finance","author":"WT Ziemba","year":"2014","unstructured":"Ziemba, W.T., Vickson, R.G.: Stochastic Optimization Models in Finance. Academic Press (2014)"}],"container-title":["Computational Optimization and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10589-023-00483-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10589-023-00483-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10589-023-00483-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T11:13:34Z","timestamp":1690283614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10589-023-00483-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["483"],"URL":"https:\/\/doi.org\/10.1007\/s10589-023-00483-2","relation":{},"ISSN":["0926-6003","1573-2894"],"issn-type":[{"value":"0926-6003","type":"print"},{"value":"1573-2894","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]},"assertion":[{"value":"13 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}