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This paper deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication, without any central coordinator.
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2013
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D. Mateos-Núnez and J. Cortés, “Distributed online convex optimization over jointly connected digraphs,” IEEE Transactions on Network Science and Engineering
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
2014
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M. Akbari, B. Gharesifard, and T. Linder, “Distributed online convex optimization on time-varying directed graphs,” IEEE Transactions on Control of Network Systems
2015
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2015
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2015
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2016
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2016
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P. Di Lorenzo and G. Scutari, “Next: In-network nonconvex optimization,” IEEE Transactions on Signal and Information Processing over Networks
2016
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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 (CDC)
2016
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M. Fazlyab, S. Paternain, V. M. Preciado, and A. Ribeiro, “Prediction-correction interior-point method for time-varying convex optimization,” IEEE Trans. on Automatic Control
2017
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S. Shahrampour and A. Jadbabaie, “Distributed online optimization in dynamic environments using mirror descent,” IEEE Transactions on Automatic Control
2017
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A. Nedić, A. Olshevsky, and W. Shi, “Achieving geometric convergence for distributed optimization over time-varying graphs,” SIAM Journal on Optimization
M. Akbari, B. Gharesifard, and T. Linder, “Individual regret bounds for the distributed online alternating direction method of multipliers,” IEEE Transactions on Automatic Control
2019
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G. Scutari and Y. Sun, “Distributed nonconvex constrained optimization over time-varying digraphs,” Mathematical Programming
2019
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S. S. Kia, B. Van Scoy, J. Cortes, R. A. Freeman, K. M. Lynch, and S. Martinez, “Tutorial on dynamic average consensus: The problem, its applications, and the algorithms,” IEEE Control Systems Magazine
2019
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Y. Zhang, R. J. Ravier, M. M. Zavlanos, and V. Tarokh, “A distributed online convex optimization algorithm with improved dynamic regret,” in IEEE Conf. on Decision and Control (CDC)
2019
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2017
Cited alongside, same era.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Convergence of asynchronous distributed gradient methods over stochastic networks,” IEEE Transactions on Automatic Control
2017
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2017
Cited alongside, same era.
G. Qu and N. Li, “Harnessing Smoothness to Accelerate Distributed Optimization,” IEEE Transactions on Control of Network Systems
2018
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R. Xin and U. A. Khan, “A linear algorithm for optimization over directed graphs with geometric convergence,” IEEE Control Systems Letters
2018
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2018
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X. Chen, S. Liu, R. Sun, and M. Hong, “On the convergence of a class of adam-type algorithms for non-convex optimization,” in International Conference on Learning Representations
2018
Cited alongside, same era.
S. J. Reddi, S. Kale, and S. Kumar, “On the convergence of adam and beyond,” in International Conference on Learning Representations
2018
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2019
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X. Yi, X. Li, L. Xie, and K. H. Johansson, “Distributed online convex optimization with time-varying coupled inequality constraints,” IEEE Transactions on Signal Processing
2020
Closest in time.
S. Pu and A. Nedić, “Distributed stochastic gradient tracking methods,” Mathematical Programming
2020
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E. Dall’Anese, A. Simonetto, S. Becker, and L. Madden, “Optimization and learning with information streams: Time-varying algorithms and applications,” IEEE Signal Processing Magazine
2020
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Y. Li, G. Qu, and N. Li, “Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit,” IEEE Transactions on Automatic Control
2020
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F. Farina and G. Notarstefano, “Randomized block proximal methods for distributed stochastic big-data optimization,” IEEE Transactions on Automatic Control
2021
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A. Simonetto, E. Dall’Anese, J. Monteil, and A. Bernstein, “Personalized optimization with user’s feedback,” Automatica
2021
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2022
Closest in time.
I. Notarnicola, A. Simonetto, F. Farina, and G. Notarstefano, “Distributed personalized gradient tracking with convex parametric models,” IEEE Transactions on Automatic Control
2022
Closest in time.