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This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory.
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Accelerated linear iterations for distributed averaging
Ji Liu and A. Stephen Morse · 2011
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Decentralized multi-agent optimization via dual decomposition
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Konstantinos I. Tsianos, Sean Lawlor, and Michael G. Rabbat · 2012
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Fast distributed gradient methods
Dušan Jakovetić, Joao Xavier, and José M.F. Moura · 2014
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On the linear convergence of the admm in decentralized consensus optimization
Wei Shi, Qing Ling, Kun Yuan, Gang Wu, and Wotao Yin · 2014
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Paolo Di Lorenzo and Gesualdo Scutari · 2015
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Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes
Jinming Xu, Shanying Zhu, Yeng Chai Soh, and Lihua Xie · 2015
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Next: In-network nonconvex optimization
Paolo Di Lorenzo and Gesualdo Scutari · 2016
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Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation
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On the convergence of decentralized gradient descent
A linearly convergent proximal gradient algorithm for decentralized optimization
Sulaiman A. Alghunaim, Kun Yuan, and Ali H. Sayed · 2019
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A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Zhi Li, Wei Shi, and Ming Yan · 2019
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Accelerated distributed Nesterov gradient descent
Guannan Qu and Na Li · 2019
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Optimal convergence rates for convex distributed optimization in networks
Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Lee, and Laurent Massoulié · 2019
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Decentralized proximal gradient algorithms with linear convergence rates
Sulaiman A. Alghunaim, Ernest Ryu, Kun Yuan, and Ali H. Sayed · 2020
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Optimal and practical algorithms for smooth and strongly convex decentralized optimization
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