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Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design.
Equation of state calculations by fast computing machines
N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller · 1953
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Chaotic relaxation
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Asynchronous iterative methods for multiprocessors
G. M. Baudet · 1978
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Distributed asynchronous computation of fixed points
D. P. Bertsekas · 1983
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Distributed asynchronous deterministic and stochastic gradient optimization algorithms
J. Tsitsiklis, D. Bertsekas, and M. Athans · 1986
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Fast linear iterations for distributed averaging
L. Xiao and S. Boyd · 2004
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The Perron-Frobenius theorem: Some of its applications
S. U. Pillai, T. Suel, and S. Cha · 2005
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Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
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Parallelized stochastic gradient descent
M. Zinkevich, M. Weimer, L. Li, and A. J. Smola · 2010
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Distributed optimization and statistical learning via alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
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Distributed delayed stochastic optimization
A. Agarwal and J. C. Duchi · 2011
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Decentralized optimal dispatch of distributed energy resources
A. D. Dominguez-Garcia, S. T. Cady, and C. N. Hadjicostis · 2012
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Dual averaging for distributed optimization: Convergence analysis and network scaling
J. C. Duchi, A. Agarwal, and M. J. Wainwright · 2012
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A fast distributed proximal-gradient method
A. I. Chen and A. Ozdaglar · 2012
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Distributed Pareto optimization via diffusion strategies
J. Chen and A. H. Sayed · 2013
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A primal-dual fixed point algorithm for convex separable minimization with applications to image restoration
P. Chen, J. Huang, and X. Zhang · 2013
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Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2013
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Communication-efficient distributed optimization using an approximate Newton-type method
O. Shamir, N. Srebro, and T. Zhang · 2014
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Scaling distributed machine learning with the parameter server
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su · 2014
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On the linear convergence of the ADMM in decentralized consensus optimization
W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin · 2014
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On the convergence of decentralized gradient descent
K. Yuan, Q. Ling, and W. Yin · 2016
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On the ergodic convergence rates of a first-order primal–dual algorithm
A. Chambolle and T. Pock · 2016
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Parallel multi-block ADMM with o (1/k) convergence
W. Deng, M.-J. Lai, Z. Peng, and W. Yin · 2017
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Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu · 2017
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Achieving geometric convergence for distributed optimization over time-varying graphs
A. Nedic, A. Olshevsky, and W. Shi · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
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A proximal stochastic gradient method with progressive variance reduction
L. Xiao and T. Zhang · 2014
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Disco: Distributed optimization for self-concordant empirical loss
Y. Zhang and X. Lin · 2015
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Linear convergence rate of a class of distributed augmented Lagrangian algorithms
D. Jakovetić, J. M. Moura, and J. Xavier · 2015
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DLM: Decentralized linearized alternating direction method of multipliers
Q. Ling, W. Shi, G. Wu, and A. Ribeiro · 2015
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Multi-agent distributed optimization via inexact consensus ADMM
T.-H. Chang, M. Hong, and X. Wang · 2015
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Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes
J. Xu, S. Zhu, Y. C. Soh, and L. Xie · 2015
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On the convergence rate improvement of a primal-dual splitting algorithm for solving monotone inclusion problems
R. I. Bot, E. R. Csetnek, A. Heinrich, and C. Hendrich · 2015
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K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, and L. Massoulie · 2017
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Z. Li and M. Yan · 2017
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A distributed quasi-newton algorithm for empirical risk minimization with nonsmooth regularization
C.-P. Lee, C. H. Lim, and S. J. Wright · 2018
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CoCoA: A general framework for communication-efficient distributed optimization
V. Smith, S. Forte, M. Chenxin, M. Takac, M. I. Jordan, and M. Jaggi · 2018
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Asynchronous decentralized parallel stochastic gradient descent
X. Lian, W. Zhang, C. Zhang, and J. Liu · 2018
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Harnessing smoothness to accelerate distributed optimization
G. Qu and N. Li · 2018
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COLA: Decentralized linear learning
L. He, A. Bian, and M. Jaggi · 2018
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Distributed linearized alternating direction method of multipliers for composite convex consensus optimization
N. S. Aybat, Z. Wang, T. Lin, and S. Ma · 2018
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A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Z. Li, W. Shi, and M. Yan · 2019
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A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization
P. Latafat, N. M. Freris, and P. Patrinos · 2019
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