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Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks.
Introductory lectures on convex programming volume i: Basic course
Yurii Nesterov · 1998
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Distributed learning in wireless sensor networks
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Distributed subgradient methods for multi-agent optimization
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Consensus-based distributed support vector machines
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Multirobot active target tracking with combinations of relative observations
Ke Zhou and Stergios I Roumeliotis · 2011
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A fast distributed proximal-gradient method
Annie I Chen and Asuman Ozdaglar · 2012
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Matrix analysis
Roger A Horn and Charles R Johnson · 2012
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Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning
Konstantinos I Tsianos, Sean Lawlor, and Michael G Rabbat · 2012
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On the o (1= k) convergence of asynchronous distributed alternating direction method of multipliers
Ermin Wei and Asuman Ozdaglar · 2013
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Proximal-gradient algorithms for tracking cascades over social networks
Brian Baingana, Gonzalo Mateos, and Georgios B Giannakis · 2014
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Linear convergence rate of a class of distributed augmented lagrangian algorithms
Dušan Jakovetić, José MF Moura, and Joao Xavier · 2014
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Diffusion adaptation over networks
Ali H Sayed · 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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Parallel and distributed computation: numerical methods
Dimitri Bertsekas and John Tsitsiklis · 2015
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Dlm: Decentralized linearized alternating direction method of multipliers
Qing Ling, Wei Shi, Gang Wu, and Alejandro Ribeiro · 2015
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Extra: An exact first-order algorithm for decentralized consensus optimization
Wei Shi, Qing Ling, Gang Wu, and Wotao Yin · 2015
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Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes
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Next: In-network nonconvex optimization
Paolo Di Lorenzo and Gesualdo Scutari · 2016
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Dheeru Dua and Casey Graff · 2017
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Computational convergence analysis of distributed optimization algorithms for directed graphs
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Decentralized proximal gradient algorithms with linear convergence rates
Sulaiman A Alghunaim, Ernest K Ryu, Kun Yuan, and Ali H Sayed · 2020
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Ideal: Inexact decentralized accelerated augmented lagrangian method
Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka, and Hongzhou Lin · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Push–pull gradient methods for distributed optimization in networks
Shi Pu, Wei Shi, Jinming Xu, and Angelia Nedić · 2020
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Bandwidth Limited Distributed Optimization with Applications to Networked Cyberphysical Systems
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Geometrically convergent distributed optimization with uncoordinated step-sizes
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Harnessing smoothness to accelerate distributed optimization
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On the convergence of nested decentralized gradient methods with multiple consensus and gradient steps
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Local sgd: Unified theory and new efficient methods
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A decentralized primal-dual framework for non-convex smooth consensus optimization
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Flexpd: A flexible framework of first-order primal-dual algorithms for distributed optimization
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Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients
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Distributed algorithms for composite optimization: Unified framework and convergence analysis
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On the performance of gradient tracking with local updates
Edward Duc Hien Nguyen, Sulaiman A Alghunaim, Kun Yuan, and César A Uribe · 2022
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Distributed optimization based on gradient tracking revisited: Enhancing convergence rate via surrogation
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