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This work studies a class of non-smooth decentralized multi-agent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common non-smooth term.
A. Sundararajan, B. Van Scoy, and L. Lessard, “Analysis and design of first-order distributed optimization algorithms over time-varying graphs,” · 1907
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F. S. Cattivelli and A. H. Sayed, “Diffusion LMS strategies for distributed estimation,”
2010
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Y. Nesterov, · 2013
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A. H. Sayed, “Adaptation, learning, and optimization over neworks.,”
2014
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J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,” in
2015
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W. Shi, Q. Ling, G. Wu, and W. Yin, “Extra: An exact first-order algorithm for decentralized consensus optimization,”
2015
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Q. Ling, W. Shi, G. Wu, and A. Ribeiro, “DLM: Decentralized linearized alternating direction method of multipliers,”
2015
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W. Shi, Q. Ling, G. Wu, and W. Yin, “A proximal gradient algorithm for decentralized composite optimization,”
2015
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T.-H. Chang, M. Hong, and X. Wang, “Multi-agent distributed optimization via inexact consensus ADMM,”
2015
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Y. Arjevani and O. Shamir, “Communication complexity of distributed convex learning and optimization,” in
2015
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P. Di Lorenzo and G. Scutari, “Next: In-network nonconvex optimization,”
2016
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Y. Sun, G. Scutari, and D. Palomar, “Distributed nonconvex multiagent optimization over time-varying networks,” in
2016
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P. Bianchi, W. Hachem, and F. Iutzeler, “A coordinate descent primal-dual algorithm and application to distributed asynchronous optimization,”
2016
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B. Woodworth and N. Srebro, “Tight complexity bounds for optimizing composite objectives,” in
2016
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N. Loizou and P. Richtárik, “A new perspective on randomized gossip algorithms,” in
2016
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A. Nedić, A. Olshevsky, W. Shi, and C. A. Uribe, “Geometrically convergent distributed optimization with uncoordinated step-sizes,” in
2017
Cited alongside, same era.
G. Scutari and Y. Sun, “Distributed nonconvex constrained optimization over time-varying digraphs,”
2019
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K. Yuan, B. Ying, X. Zhao, and A. H. Sayed, “Exact diffusion for distributed optimization and learning-Part I: Algorithm development,”
2019
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Z. Li, W. Shi, and M. Yan, “A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates,”
2019
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Y. Sun, A. Daneshmand, and G. Scutari, “Convergence rate of distributed optimization algorithms based on gradient tracking,”
2019
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D. Jakovetic, “A unification and generalization of exact distributed first-order methods,”
2019
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A. Nedic, A. Olshevsky, and W. Shi, “Achieving geometric convergence for distributed optimization over time-varying graphs,”
2017
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A. Beck, · 2017
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G. Qu and N. Li, “Harnessing smoothness to accelerate distributed optimization,”
2018
Cited alongside, same era.
S. Pu, W. Shi, J. Xu, and A. Nedić, “A push-pull gradient method for distributed optimization in networks,” in
2018
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R. Xin and U. A. Khan, “A linear algorithm for optimization over directed graphs with geometric convergence,”
2018
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N. S. Aybat, Z. Wang, T. Lin, and S. Ma, “Distributed linearized alternating direction method of multipliers for composite convex consensus optimization,”
2018
Cited alongside, same era.
A. Sundararajan, B. Van Scoy, and L. Lessard, “A canonical form for first-order distributed optimization algorithms,” in
2019
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Also available on arXiv:1905.07996, May 2019
S. A. Alghunaim, K. Yuan, and A. H. Sayed, “A linearly convergent proximal gradient algorithm for decentralized optimization,” in · 2019
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K. Yuan, B. Ying, X. Zhao, and A. H. Sayed, “Exact diffusion for distributed optimization and learning-Part II: Convergence analysis,”
2019
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P. Latafat, N. M. Freris, and P. Patrinos, “A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization,”
2019
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C. A. Uribe, S. Lee, A. Gasnikov, and A. Nedić, “A dual approach for optimal algorithms in distributed optimization over networks,”
2020
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