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Emerging applications in multi-agent environments such as internet-of-things, networked sensing, autonomous systems and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource-efficient in terms of both computation and communication.
Fast linear iterations for distributed averaging
L. Xiao and S. Boyd · 2004
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PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization
Z. Li, H. Bao, X. Zhang, and P. Richtárik · 2008
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Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
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Discrete-time dynamic average consensus
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The MNIST database of handwritten digit images for machine learning research [best of the web]
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Accelerating stochastic gradient descent using predictive variance reduction
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Chebyshev acceleration of iterative refinement
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
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EXTRA: An exact first-order algorithm for decentralized consensus optimization
W. Shi, Q. Ling, G. Wu, and W. Yin · 2015
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Variance reduction for faster non-convex optimization
Z. Allen-Zhu and E. Hazan · 2016
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Next: In-network nonconvex optimization
P. Di Lorenzo and G. Scutari · 2016
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Stochastic variance reduction for nonconvex optimization
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola · 2016
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Fast incremental method for smooth nonconvex optimization
S. J. Reddi, S. Sra, B. Póczos, and A. Smola · 2016
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Non-convex finite-sum optimization via SCSG methods
L. Lei, C. Ju, J. Chen, and M. I. Jordan · 2017
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Distributed stochastic variance reduced gradient methods by sampling extra data with replacement
J. D. Lee, Q. Lin, T. Ma, and T. Yang · 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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SARAH: A novel method for machine learning problems using stochastic recursive gradient
L. M. Nguyen, J. Liu, K. Scheinberg, and M. Takáč · 2017
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Achieving geometric convergence for distributed optimization over time-varying graphs
A. Nedić, A. Olshevsky, and W. Shi · 2017
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SPIDER: Near-optimal non-convex optimization via stochastic path-integrated differential estimator
C. Fang, C. J. Li, Z. Lin, and T. Zhang · 2018
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A simple proximal stochastic gradient method for nonsmooth nonconvex optimization
Z. Li and J. Li · 2018
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Convergence of distributed stochastic variance reduced methods without sampling extra data
S. Cen, H. Zhang, Y. Chi, W. Chen, and T.-Y. Liu · 2020
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On the benefits of multiple gossip steps in communication-constrained decentralized optimization
A. Hashemi, A. Acharya, R. Das, H. Vikalo, S. Sanghavi, and I. Dhillon · 2020
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Communication-efficient distributed optimization in networks with gradient tracking and variance reduction
B. Li, S. Cen, Y. Chen, and Y. Chi · 2020
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Scaling-up distributed processing of data streams for machine learning
M. Nokleby, H. Raja, and W. U. Bajwa · 2020
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D-SPIDER-SFO: A decentralized optimization algorithm with faster convergence rate for nonconvex problems
T. Pan, J. Liu, and J. Wang · 2020
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Network topology and communication-computation tradeoffs in decentralized optimization
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Harnessing smoothness to accelerate distributed optimization
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D 2 {D}^{2} : Decentralized training over decentralized data
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Stochastic nested variance reduction for nonconvex optimization
D. Zhou, P. Xu, and Q. Gu · 2018
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SSRGD: Simple stochastic recursive gradient descent for escaping saddle points
Z. Li · 2019
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Improving the sample and communication complexity for decentralized non-convex optimization: Joint gradient estimation and tracking
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Decentralized stochastic optimization and machine learning: A unified variance-reduction framework for robust performance and fast convergence
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Fast decentralized non-convex finite-sum optimization with recursive variance reduction
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A fast randomized incremental gradient method for decentralized non-convex optimization
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A general framework for decentralized optimization with first-order methods
R. Xin, S. Pu, A. Nedić, and U. A. Khan · 2020
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On the convergence of nested decentralized gradient methods with multiple consensus and gradient steps
A. S. Berahas, R. Bollapragada, and E. Wei · 2021
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S-NEAR-DGD: A flexible distributed stochastic gradient method for inexact communication
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A short note of PAGE: Optimal convergence rates for nonconvex optimization
Z. Li · 2021
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ZeroSARAH: Efficient nonconvex finite-sum optimization with zero full gradient computation
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