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Decentralized optimization over time-varying graphs has been increasingly common in modern machine learning with massive data stored on millions of mobile devices, such as in federated learning.
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Introductory Lectures on Convex Optimization: A Basic Course
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Nedić, A. and Ozdaglar, A · 2009
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Ram, S. S., Nedić, A., and Veeravalli, V. V · 2010
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Nedić, A · 2011
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Terelius, H., Topcu, U., and Murray, R. M · 2011
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Dual averaging for distributed optimization: Convergence analysis and network scaling
Duchi, J., Agarwal, A., and Wainwright, M · 2012
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On the o ( 1 / k ) o(1/k) convergence of asynchronous distributed alternating direction method of multipliers
Wei, E. and Ozdaglar, A · 2013
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Chebyshev acceleration of iterative refinement
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Devolder, O., Glineur, F., and Nesterov, Y · 2014
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Distributed optimization over time-varying directed graphs
Nedić, A. and Olshevsky, A · 2015
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Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes
Xu, J., Zhu, S., Soh, Y. C., and Xie, L · 2015
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Explicit convergence rate of a distributed alternating direction method of multipliers
Iutzeler, F., Bianchi, P., Ciblat, P., and Hachem, W · 2016
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NEXT: In-network nonconvex optimization
Lorenzo, P. D. and Scutari, G · 2016
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Stochastic gradient-push for strongly convexfunctions on time-varying directed graphs
Nedić, A. and Olshevsky, A · 2016
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On the convergence of decentralized gradient descent
Yuan, K., Ling, Q., and Yin, W · 2016
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Iterative solution of large linear systems
Auzinger, W. and Melenk, J. M · 2017
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Stochastic proximal gradient consensus over random networks
Hong, M. and Chang, T.-H · 2017
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Prox-PDA: The proximal primal-dual algorithm for fast distributed nonconvex optimization and learning over networks
Hong, M., Hajinezhad, D., and Zhao, M.-M · 2017
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Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J · 2017
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Convergence rate of distributed ADMM over networks
Makhdoumi, A. and Ozdaglar, A · 2017
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Achieving geometric convergence for distributed optimization over time-varying graphs
Nedić, A., Olshevsky, A., and Shi, W · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L · 2017
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PANDA: A dual linearly converging method for distributed optimization over time-varying undirected graphs
Maros, M. and Jalden, J · 2018
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Revisiting EXTRA for smooth distributed optimization
Li, H. and Lin, Z · 2020
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Accelerated Optimization in Machine Learning: First-Order Algorithms
Lin, Z., Li, H., and Fang, C · 2020
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Accelerated distributed Nesterov gradient descent
Qu, G. and Li, N · 2020
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Optimal distributed convex optimization on slowly time-varying graphs
Rogozin, A., Uribe, C. A., Gasnikov, A. V., Malkovsky, N., and Nedić, A · 2020
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Decentralized optimization over time-varying directed graphs with row and column-stochastic matrices
Saadatniaki, F., Xin, R., and Khan, U. A · 2020
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Robust asynchronous stochastic gradient push: Asymptotically optimal and network independent performance for strongly convex functions
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Qu, G. and Li, N · 2018
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Optimal algorithms for non-smooth distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L · 2018
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A linear algorithm for optimization over directed graphs with geometric convergence
Xin, R., Khan, U. A., and Kar, S · 2018
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Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., and et al · 2019
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A unification and generatliztion of exact distributed first order methods
Jakovetić, D · 2019
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A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Li, Z., Shi, W., and Yan, M · 2019
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Eco-panda: A computationally economic, geometrically converging dual optimization method on time-varying undirected graphs
Maros, M. and Jalden, J · 2019
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Spiridonoff, A., Olshevsky, A., and Paschalidis, I. C · 2020
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Accelerated primal-dual algorithms for distributed smooth convex optimization over networks
Xu, J., Tian, Y., Sun, Y., and Scutari, G · 2020
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Decentralized accelerated proximal gradient descent
Ye, H., Zhou, Z., Luo, L., and Zhang, T · 2020
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Decentralized proximal gradient algorithms with linear covnergence rates
Alghunaim, S. A., Ryu, E. K., Yuan, K., and H.Sayed, A · 2021
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Decentralized and parallelized primal and dual accelerated methods for stochastic convex programming problems
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An optimal algorithm for decentralized finite sum optimization
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., and et al · 2021
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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 · 2021
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A dual approach for optimal algorithms in distributed optimization over networks
Uribe, C. A., Lee, S., Gasnikov, A., and Nedić, A · 2021
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Communication-efficient variance-reduced decentralized stochastic optimization over time-varying directed graphs
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Robust distributed accelerated stochastic gradient methods for multi-agent networks
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Variance reduced EXTRA and DIGing and their optimal acceleration for strongly convex decentralized optimization
Li, H., Lin, Z., and Fang, Y · 2022
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Distributed optimization based on gradient-tracking revisited: Enhancing convergence rate via surrogation
Sun, Y., Scutari, G., and Daneshmand, A · 2022
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Multi-consensus decentralized accelerated gradient descent
Ye, H., Luo, L., Zhou, Z., and Zhang, T · 2023
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