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We study distributed multiagent optimization over (directed, time-varying) graphs.
G. Scutari, F. Facchinei, and L. Lampariello, Parallel and distributed methods for constrained nonconvex optimization–Part I: Theory , IEEE Trans. Signal Process. 65 (2017), pp. 1929–1944
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C.G. Lopes and A.H. Sayed, Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis , IEEE Trans. Signal Process. 56 (2008), pp. 3122–3136
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A. Nedić and A. Ozdaglar, Distributed subgradient methods for multi-agent optimization , IEEE Trans. Autom. Control 54 (2009), pp. 48–61
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S. Shalev-Shwartz, O. Shamir, N. Srebro, and K. Sridharan, Stochastic Convex Optimization , in Proc. of the 22nd Annual Conference on Learning Theory (COLT) , June 18-21, Montreal, Canada. 2009
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B. Gharesifard and J. Cortés, When does a digraph admit a doubly stochastic adjacency matrix? , in Proc. of the 2010 American Control Conference , June. 2010, pp. 2440–2445
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A. Nedić, A. Ozdaglar, and P.A. Parrilo, Constrained consensus and optimization in multi-agent networks , IEEE Trans. Autom. Control 55 (2010), pp. 922–938
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A. Nedić and A. Ozdaglar, Convergence rate for consensus with delays , Journal of Global Optimization 47 (2010), pp. 437–456
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D. Jakovetic, J. Xavier, and J.M. Moura, Cooperative convex optimization in networked systems: Augmented Lagrangian algorithms with directed gossip communication , IEEE Trans. Signal Process. 59 (2011), pp. 3889–3902
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A. Wien, Iterative solution of large linear systems , Lecture Notes, TU Wien, 2011
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O. Shamir, N. Srebro, and T. Zhang, Communication-Efficient Distributed Optimization using an Approximate Newton-type Method , in Proc. of the 31st International Conference on Machine Learning (PMLR) , Vol. 32. 2014, pp. 1000–1008
2014
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W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, On the linear convergence of the ADMM in decentralized consensus optimization , IEEE Trans. Signal Process. 62 (2014), pp. 1750–1761
2014
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P. Di Lorenzo and G. Scutari, Distributed nonconvex optimization over networks , in Proc. of 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) , Dec., Cancun. 2015, pp. 229–232
2015
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F. Facchinei, G. Scutari, and S. Sagratella, Parallel selective algorithms for nonconvex big data optimization , IEEE Trans. Signal Process. 63 (2015), pp. 1874–1889
2015
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D. Jakovetic, J.M.F. Moura, and J. Xavier, Linear convergence rate of a class of distributed augmented lagrangian algorithms , IEEE Trans. Autom. Control 60 (2015), pp. 922–936
2015
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Q. Ling, W. Shi, G. Wu, and A. Ribeiro, DLM: Decentralized linearized alternating direction method of multipliers , IEEE Trans. Signal Process. 63 (2015), pp. 4051–4064
2015
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A. Nedic and A. Olshevsky, Distributed optimization over time-varying directed graphs , IEEE Trans. Autom. Control 60 (2015), pp. 601–615
2015
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W. Shi, Q. Ling, G. Wu, and W. Yin, EXTRA: An exact first-order algorithm for decentralized consensus optimization , SIAM J. Optim. 25 (2015), pp. 944–966
2015
Earlier work this paper cites.
W. Shi, Q. Ling, G. Wu, and W. Yin, A proximal gradient algorithm for decentralized composite optimization , IEEE Trans. Signal Process. 63 (2015), pp. 6013–6023
2015
Cited alongside, same era.
Y. Zhang and X. Lin, DiSCO: Distributed Optimization for Self-Concordant Empirical Loss , in Proc. of the 32nd International Conference on Machine Learning (PMLR) , Vol. 37. 2015, pp. 362–370
2015
Cited alongside, same era.
P. Di Lorenzo and G. Scutari, NEXT: In-network nonconvex optimization , IEEE Trans. Signal Inf. Process. Netw. 2 (2016), pp. 120–136
2016
Cited alongside, same era.
A. Mokhtari, W. Shi, Q. Ling, and A. Ribeiro, Dqm: Decentralized quadratically approximated alternating direction method of multipliers , IEEE Transactions on Signal Processing 64 (2016), pp. 5158–5173
2016
Cited alongside, same era.
2018
Later among the works it cites.
J. Xu, S. Zhu, Y.C. Soh, and L. Xie, Convergence of Asynchronous Distributed Gradient Methods Over Stochastic Networks , IEEE Trans. Autom. Control 63 (2018), pp. 434–448
2018
Later among the works it cites.
K. Yuan, B. Ying, X. Zhao, and A.H. Sayed, Exact diffusion for distributed optimization and learning—part ii: Convergence analysis , IEEE Transactions on Signal Processing 67 (2018), pp. 724–739
2018
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2018
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G. Qu and N. Li, Accelerated Distributed Nesterov Gradient Descent for smooth and strongly convex functions , in 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) , Sept. 2016, pp. 209–216
2016
Cited alongside, same era.
Y. Sun, G. Scutari, and D. Palomar, Distributed nonconvex multiagent optimization over time-varying networks , in Proc. of the Asilomar Conference on Signals, Systems, and Computers (2016)
2016
Cited alongside, same era.
K. Yuan, Q. Ling, and W. Yin, On the Convergence of Decentralized Gradient Descent , SIAM J. Optim. 26 (2016), pp. 1835–1854
2016
Cited alongside, same era.
A. Nedić, A. Olshevsky, and W. Shi, Achieving geometric convergence for distributed optimization over time-varying graphs , SIAM Journal on Optimization 27 (2017), pp. 2597–2633
2017
Cited alongside, same era.
A. Nedić, A. Olshevsky, W. Shi, and C.A. Uribe, Geometrically convergent distributed optimization with uncoordinated step-sizes , in 2017 American Control Conference . 2017, pp. 3950–3955
2017
Cited alongside, same era.
K. Scaman, F. Bach, S. Bubeck, Y.T. Lee, and L. Massoulié, Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks , in Proc. of the 34th International Conference on Machine Learning , Vol. 70. 2017, pp. 3027–3036
2017
Cited alongside, same era.
J. Zeng and W. Yin, ExtraPush for convex smooth decentralized optimization over directed networks , J. Comput. Math. 35 (2017), pp. 383–396
2017
Cited alongside, same era.
M. Maros and J. Jalden, PANDA: A Dual Linearly Converging Method for Distributed Optimization Over Time-Varying Undirected Graphs , 2018 IEEE Conference on Decision and Control (CDC) (2018), pp. 6520–6525
2018
Cited alongside, same era.
2019
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A. Berahas, R. Bollapragada, N.S. Keskar, and E. Wei, Balancing Communication and Computation in Distributed Optimization , IEEE Trans. Autom. Control (to appear, 2019)
2019
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2019
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D. Jakovetic, A Unification and Generalization of Exact Distributed First-Order Methods , IEEE Trans. Signal Inf. Process. Netw. 5 (2019), pp. 31–46
2019
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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 , IEEE Transactions on Signal Processing 67 (2019), pp. 4494–4506
2019
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M. Maros and J. Jalden, On the Q-linear convergence of Distributed Generalized ADMM under non-strongly convex function components , IEEE Trans. Signal Inf. Process. Netw. PP (2019), pp. 1–1
2019
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2019
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G. Scutari and Y. Sun, Distributed nonconvex constrained optimization over time-varying digraphs , Math. Prog. 176 (2019), pp. 497–544
2019
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2019
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2020
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H. Lu, R.M. Freund, and Y. Nesterov, Relatively smooth convex optimization by first-order methods, and applications , SIAM J. on Optimization 28 (2020), pp. 333–354
2020
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F. Saadatniaki, R. Xin, and U.A. Khan, Decentralized optimization over time-varying directed graphs with row and column-stochastic matrices , IEEE Transactions on Automatic Control (2020), pp. 1–1
2020
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Y. Tian, Y. Sun, and G. Scutari, Achieving linear convergence in distributed asynchronous multi-agent optimization , IEEE Trans. on Automatic Control (2020)
2020
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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 Proc. of the 54th IEEE Conference on Decision and Control (CDC 2015) , Dec., Osaka, Japan. 2015, pp. 2055–2060
2060
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