Fetching the paper…
Reading the bibliography…
This paper focuses on the online gradient and proximal-gradient methods with stochastic gradient errors.
D. P. Bertsekas and J. N. Tsitsiklis, “Gradient convergence in gradient methods with errors,” in SIAM J. Optim , 1997
1997
Earlier work this paper cites.
A. Y. Popkov, “Gradient methods for nonstationary unconstrained optimization problems,” Automation and Remote Control , vol. 66, no. 6, pp. 883–891, 2005
2005
Earlier work this paper cites.
J. Bolte, A. Daniilidis, and A. Lewis, “The łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems,” SIAM Journal on Optimization , vol. 17, no. 4, pp. 1205–1223, 2007
2007
Earlier work this paper cites.
J. Bolte, A. Daniilidis, O. Ley, and L. Mazet, “Characterizations of łojasiewicz inequalities: subgradient flows, talweg, convexity,” Transactions of the American Mathematical Society , vol. 362, no. 6, pp. 3319–3363, 2010
2010
Earlier work this paper cites.
M. Schmidt, N. L. Roux, and F. R. Bach, “Convergence rates of inexact proximal-gradient methods for convex optimization,” in Advances in neural information processing systems , 2011, pp. 1458–1466
2011
Earlier work this paper cites.
E. Moulines and F. Bach, “Non-asymptotic analysis of stochastic approximation algorithms for machine learning,” Advances in neural information processing systems , vol. 24, 2011
2011
Earlier work this paper cites.
Z. J. Towfic, J. Chen, and A. H. Sayed, “On distributed online classification in the midst of concept drifts,” Neurocomputing , vol. 112, pp. 138–152, 2013
2013
Earlier work this paper cites.
H. Attouch, J. Bolte, and B. F. Svaiter, “Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized gauss–seidel methods,” Mathematical Programming , vol. 137, no. 1, pp. 91–129, 2013
2013
Earlier work this paper cites.
O. Devolder, F. Glineur, and Y. Nesterov, “First-order methods of smooth convex optimization with inexact oracle,” Mathematical Programming , vol. 146, no. 1, pp. 37–75, 2014
2014
Earlier work this paper cites.
O. Besbes, Y. Gur, and A. Zeevi, “Non-stationary stochastic optimization,” Operations research , vol. 63, no. 5, pp. 1227–1244, 2015
2015
Earlier work this paper cites.
S. Bolognani, R. Carli, G. Cavraro, and S. Zampieri, “Distributed reactive power feedback control for voltage regulation and loss minimization,” IEEE Trans. on Automatic Control , vol. 60, no. 4, pp. 966–981, Apr. 2015
2015
Earlier work this paper cites.
T. Yang, L. Zhang, R. Jin, and J. Yi, “Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient,” in International Conference on Machine Learning , 2016
2016
Earlier work this paper cites.
A. Mokhtari, S. Shahrampour, A. Jadbabaie, and A. Ribeiro, “Online optimization in dynamic environments: Improved regret rates for strongly convex problems,” in IEEE Conference on Decision and Control , 2016, pp. 7195–7201
2016
Earlier work this paper cites.
H. Karimi, J. Nutini, and M. Schmidt, “Linear convergence of gradient and proximal-gradient methods under the Polyak-Łojasiewicz condition,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2016, pp. 795–811
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Y. F. Atchadé, G. Fort, and E. Moulines, “On perturbed proximal gradient algorithms,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 310–342, 2017
2017
Cited alongside, same era.
Y. Li and Y. Yuan, “Convergence analysis of two-layer neural networks with ReLU activation,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Cited alongside, same era.
Y. Tang, J. Zhang, and N. Li, “Distributed zero-order algorithms for nonconvex multiagent optimization,” IEEE Transactions on Control of Network Systems , vol. 8, no. 1, pp. 269–281, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Vladimirova, S. Girard, H. Nguyen, and J. Arbel, “Sub-weibull distributions: Generalizing sub-gaussian and sub-exponential properties to heavier tailed distributions,” Stat , vol. 9, no. 1, p. e318, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. S. Bedi, P. Sarma, and K. Rajawat, “Tracking moving agents via inexact online gradient descent algorithm,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, no. 1, pp. 202–217, 2018
2018
Cited alongside, same era.
D. D. Selvaratnam, I. Shames, J. H. Manton, and M. Zamani, “Numerical optimisation of time-varying strongly convex functions subject to time-varying constraints,” in IEEE Conference on Decision and Control , 2018, pp. 849–854
2018
Cited alongside, same era.
R. Vershynin, High-Dimensional Probability: An Introduction with Applications in Data Science , 1st ed. Cambridge University Press, Sep. 2018
2018
Cited alongside, same era.
E. Dall’Anese and A. Simonetto, “Optimal power flow pursuit,” IEEE Transactions on Smart Grid , vol. 9, no. 2, pp. 942–952, March 2018
2018
Cited alongside, same era.
L. Rosasco, S. Villa, and B. C. Vũ, “Convergence of stochastic proximal gradient algorithm,” Appl. Math. Optim , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Cited alongside, same era.
S. Vlaski, E. Rizk, and A. H. Sayed, “Tracking performance of online stochastic learners,” IEEE Signal Processing Letters , vol. 27, pp. 1385–1389, 2020
2020
Cited alongside, same era.
T.-J. Chang and S. Shahrampour, “On online optimization: Dynamic regret analysis of strongly convex and smooth problems,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 8, 2021, pp. 6966–6973
2021
Closest in time.
X. Cao, J. Zhang, and H. V. Poor, “Online stochastic optimization with time-varying distributions,” IEEE Tran. on Automatic Control , vol. 66, no. 4, pp. 1840–1847, 2021
2021
Closest in time.
2021
Closest in time.
A. Ospina, N. Bastianello, and E. Dall’Anese, “Feedback-based optimization with sub-weibull gradient errors and intermittent updates,” arXiv preprint arXiv: 2109.06343 , 2021
2021
Closest in time.
L. Madden, S. Becker, and E. Dall’Anese, “Bounds for the tracking error of first-order online optimization methods,” Journal of Optimization Theory and Applications , vol. 189, no. 2, pp. 437–457, 2021
2021
Closest in time.
2021
Closest in time.
O. Gannot, “A frequency-domain analysis of inexact gradient methods,” Mathematical Programming , pp. 1–42, 2021
2021
Closest in time.