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This paper develops and analyzes an online distributed proximal-gradient method (DPGM) for time-varying composite convex optimization problems.
1902
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1905
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1909
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2001
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A. D. Flaxman, A. T. Kalai, and H. B. McMahan, “Online convex optimization in the bandit setting: Gradient descent without a gradient,” in Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms , ser. SODA ’05, Philadelphia, PA, USA, 2005, pp. 385–394
2005
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S. Kar and J. M. F. Moura, “Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise,” IEEE Transactions on Signal Processing , vol. 57, no. 1, pp. 355–369, 2009
2009
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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
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S. Salzo and S. Villa, “Inexact and accelerated proximal point algorithms,” Journal of Convex analysis , vol. 19, no. 4, pp. 1167–1192, 2012
2012
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A. I. Chen and A. Ozdaglar, “A fast distributed proximal-gradient method,” in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . Monticello, IL, USA: IEEE, 2012, pp. 601–608
2012
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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
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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.
M. Akbari, B. Gharesifard, and T. Linder, “Distributed online convex optimization on time-varying directed graphs,” IEEE Transactions on Control of Network Systems , vol. 4, no. 3, pp. 417–428, 2015
2015
Earlier work this paper cites.
A. Koppel, F. Y. Jakubiec, and A. Ribeiro, “A saddle point algorithm for networked online convex optimization,” IEEE Transactions on Signal Processing , vol. 63, no. 19, pp. 5149–5164, Oct 2015
2015
Cited alongside, same era.
N. Aybat, Z. Wang, and G. Iyengar, “An asynchronous distributed proximal gradient method for composite convex optimization,” in International Conference on Machine Learning , 2015, pp. 2454–2462
2015
Cited alongside, same era.
W. Shi, Q. Ling, G. Wu, and W. Yin, “A Proximal Gradient Algorithm for Decentralized Composite Optimization,” IEEE Transactions on Signal Processing , vol. 63, no. 22, pp. 6013–6023, 2015
2015
Cited alongside, same era.
S. Hosseini, A. Chapman, and M. Mesbahi, “Online distributed convex optimization on dynamic networks,” IEEE Transactions on Automatic Control , vol. 61, no. 11, pp. 3545–3550, 2016
2016
Cited alongside, same era.
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
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2019
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
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K. Yuan, Q. Ling, and W. Yin, “On the Convergence of Decentralized Gradient Descent,” SIAM Journal on Optimization , vol. 26, no. 3, pp. 1835–1854, 2016
2016
Cited alongside, same era.
A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, and A. Ribeiro, “Decentralized prediction-correction methods for networked time-varying convex optimization,” IEEE Transactions on Automatic Control , vol. 62, no. 11, pp. 5724–5738, 2017
2017
Cited alongside, same era.
S. Shahrampour and A. Jadbabaie, “Distributed online optimization in dynamic environments using mirror descent,” IEEE Transactions on Automatic Control , vol. 63, no. 3, pp. 714–725, 2017
2017
Cited alongside, same era.
J. Zeng, T. He, and M. Wang, “A fast proximal gradient algorithm for decentralized composite optimization over directed networks,” Systems & Control Letters , vol. 107, pp. 36–43, 2017
2017
Cited alongside, same era.
A. B. Taylor, J. M. Hendrickx, and F. Glineur, “Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization,” Journal of Optimization Theory and Applications , vol. 178, no. 2, pp. 455–476, 2018
2018
Cited alongside, same era.
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
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
L. Majzoobi, F. Lahouti, and V. Shah-Mansouri, “Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Node Error,” IEEE Transactions on Signal Processing , vol. 67, no. 7, pp. 1774–1784, 2019
2019
Later among the works it cites.
A. Reisizadeh, A. Mokhtari, H. Hassani, and R. Pedarsani, “An Exact Quantized Decentralized Gradient Descent Algorithm,” IEEE Transactions on Signal Processing , vol. 67, no. 19, pp. 4934–4947, Oct. 2019
2019
Later among the works it cites.
Z. Li, W. Shi, and M. Yan, “A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates,” IEEE Transactions on Signal Processing , vol. 67, no. 17, pp. 4494–4506, 2019
2019
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2020
Closest in time.
2020
Closest in time.
N. Bastianello, A. Simonetto, and R. Carli, “Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization,” in 54th Asilomar Conference on Signals, Systems and Computers , Nov. 2020
2020
Closest in time.