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We propose ADOM - an accelerated method for smooth and strongly convex decentralized optimization over time-varying networks.
Time-varying networks, i
Zadeh, L. A · 1961
Earlier work this paper cites.
Convex analysis , volume 36
Rockafellar, R. T · 1970
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Nesterov, Y · 2003
Earlier work this paper cites.
Distributed optimization in sensor networks
Rabbat, M. and Nowak, R · 2004
Earlier work this paper cites.
Distributed spectrum sensing for cognitive radio networks by exploiting sparsity
Bazerque, J. A. and Giannakis, G. B · 2009
Earlier work this paper cites.
Estimating time-varying networks
Kolar, M., Song, L., Ahmed, A., and Xing, E. P · 2010
Earlier work this paper cites.
LIBSVM: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
Earlier work this paper cites.
Recent theoretical advances in decentralized distributed convex optimization
Gorbunov, E., Rogozin, A., Beznosikov, A., Dvinskikh, D., and Gasnikov, A · 2011
Earlier work this paper cites.
Optimal decentralized protocol for electric vehicle charging
Gan, L., Topcu, U., and Low, S. H · 2012
Earlier work this paper cites.
Accelerated gradient methods and dual decomposition in distributed model predictive control
Giselsson, P., Doan, M. D., Keviczky, T., De Schutter, B., and Rantzer, A · 2013
Earlier work this paper cites.
An o ( 1 / k ) o(1/k) gradient method for network resource allocation problems
Beck, A., Nedić, A., Ozdaglar, A., and Teboulle, M · 2014
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Konečný, J., McMahan, H. B., Yu, F., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Allen-Zhu, Z · 2017
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McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Agüera y Arcas, B · 2017
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Achieving geometric convergence for distributed optimization over time-varying graphs
Nedic, A., Olshevsky, A., and Shi, W · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
Optimal distributed convex optimization on slowly time-varying graphs
Rogozin, A., Uribe, C., Gasnikov, A., Malkovskii, N., and Nedich, A · 2019
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Stich, S. U. and Karimireddy, S. P · 2019
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On biased compression for distributed learning
Beznosikov, A., Horváth, S., Richtárik, P., and Safaryan, M · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Push-pull gradient methods for distributed optimization in networks
Pu, S., Shi, W., Xu, J., and Nedic, A · 2020
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Scaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L · 2017
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Linearly converging error compensated SGD
Gorbunov, E., Kovalev, D., Makarenko, D., and Richtárik, P
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Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop
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Error compensated distributed SGD can be accelerated
Qian, X., Richtárik, P., and Zhang, T · 2020
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Towards accelerated rates for distributed optimization over time-varying networks
Rogozin, A., Lukoshkin, V., Gasnikov, A., Kovalev, D., and Shulgin, E · 2020
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Multi-consensus decentralized accelerated gradient descent
Ye, H., Luo, L., Zhou, Z., and Zhang, T · 2020
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A linearly convergent algorithm for decentralized optimization: Sending less bits for free!
Kovalev, D., Koloskova, A., Jaggi, M., Richtárik, P., and Stich, S · 2021
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