Fetching the paper…
Reading the bibliography…
This paper considers an online proximal-gradient method to track the minimizers of a composite convex function that may continuously evolve over time.
R. T. Rockafellar, “Monotone operators and the proximal point algorithm,” SIAM journal on control and optimization , vol. 14, no. 5, pp. 877–898, 1976
1976
Earlier work this paper cites.
J. J. Moreau, “Evolution Problem Associated with a Moving Convex Set in a Hilbert Space,” Journal of Differential Equations , vol. 26, pp. 347 – 374, 1977
1977
Earlier work this paper cites.
J. Hiriart-Urruty and C. Lemarechal, Convex Analysis and Minimization Algorithms II: Advanced Theory and Bundle Methods , ser. Grundlehren der mathematischen Wissenschaften. Springer Berlin Heidelberg, 1996
1996
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.
R. Jenatton, J. Mairal, G. Obozinski, and F. R. Bach, “Proximal methods for sparse hierarchical dictionary learning.” in ICML , vol. 1, 2010, p. 2
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.
S. Salzo and S. Villa, “Inexact and accelerated proximal point algorithms,” Journal of Convex analysis , vol. 19, no. 4, pp. 1167–1192, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
A. Chen and A. Ozdaglar, “A fast distributed proximal-gradient method,” 10 2012, pp. 601–608
2012
Earlier work this paper cites.
S. Villa, S. Salzo, L. Baldassarre, and A. Verri, “Accelerated and inexact forward-backward algorithms,” SIAM Journal on Optimization , vol. 23, no. 3, pp. 1607–1633, 2013
2013
Earlier work this paper cites.
N. Parikh, S. Boyd et al. , “Proximal algorithms,” Foundations and Trends® in Optimization , vol. 1, no. 3, pp. 127–239, 2014
2014
Cited alongside, same era.
V. Cevher, S. Becker, and M. Schmidt, “Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics,” IEEE Signal Processing Magazine , vol. 31, no. 5, pp. 32–43, 2014
2014
Cited alongside, same era.
A. Simonetto and G. Leus, “Double smoothing for time-varying distributed multiuser optimization,” in IEEE Global Conf. on Signal and Information Processing , Dec. 2014
2014
Cited alongside, same era.
E. C. Hall and R. M. Willett, “Online convex optimization in dynamic environments,” IEEE Journal of Selected Topics in Signal Processing , vol. 9, no. 4, pp. 647–662, 2015
2015
Cited alongside, same era.
A. Jadbabaie, A. Rakhlin, S. Shahrampour, and K. Sridharan, “Online Optimization: Competing with Dynamic Comparators,” in PMLR , no. 38, 2015, pp. 398 – 406
M. S. Nazir, I. A. Hiskens, A. Bernstein, and E. Dall’Anese, “Inner approximation of minkowski sums: A union-based approach and applications to aggregated energy resources,” in IEEE Conference on Decision and Control , 2018, pp. 5708–5715
2018
Later among the works it cites.
A. K. Sahu, D. Jakovetic, D. Bajovic, and S. Kar, “Distributed zeroth order optimization over random networks: A kiefer-wolfowitz stochastic approximation approach,” in 2018 IEEE Conference on Decision and Control (CDC) . IEEE, 2018, pp. 4951--4958
2018
Later among the works it cites.
T. Chen and G. B. Giannakis, “Bandit convex optimization for scalable and dynamic IoT management,” IEEE Internet of Things Journal , vol. 6, no. 1, pp. 1276–1286, 2018
2018
Later among the works it cites.
R. Dixit, A. S. Bedi, R. Tripathi, and K. Rajawat, “Online learning with inexact proximal online gradient descent algorithms,” IEEE Transactions on Signal Processing , vol. 67, no. 5, pp. 1338–1352, 2019
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
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
Cited alongside, same era.
E. K. Ryu and S. Boyd, “Primer on monotone operator methods,” Appl. Comput. Math , vol. 15, no. 1, pp. 3–43, 2016
2016
Cited alongside, same era.
A. Beck, First-Order Methods in Optimization . Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Vaquero and J. Cortés, “Distributed augmentation-regularization for robust online convex optimization,” IFAC-PapersOnLine , vol. 51, no. 23, pp. 230–235, 2018
2018
Cited alongside, same era.
N. K. Dhingra, S. Z. Khong, and M. R. Jovanovic, “The proximal augmented lagrangian method for nonsmooth composite optimization,” IEEE Transactions on Automatic Control , vol. 64, no. 7, pp. 2861–2868, July 2019
2019
Closest in time.
A. Bernstein, E. Dall’Anese, and A. Simonetto, “Online primal-dual methods with measurement feedback for time-varying convex optimization,” IEEE Trans. on Signal Processing , vol. 67, no. 8, pp. 1978–1991, April 2019
2019
Closest in time.
D. Hajinezhad, M. Hong, and A. Garcia, “ZONE: Zeroth order nonconvex multi-agent optimization over networks,” IEEE Transactions on Automatic Control , 2019, early access
2019
Closest in time.
I. Necoara, Y. Nesterov, and F. Glineur, “Linear convergence of first order methods for non-strongly convex optimization,” Mathematical Programming , vol. 175, no. 1-2, pp. 69–107, 2019
2019
Closest in time.
M. Fardad, F. Lin, and M. R. Jovanović, “Sparsity-promoting optimal control for a class of distributed systems,” in Proceedings of the 2011 American Control Conference . IEEE, 2011, pp. 2050–2055
2055
Closest in time.