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This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms.
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S. S. Ram, A. Nedić, and V. Veeravalli, “Distributed stochastic subgradient projection algorithms for convex optimization,” Journal of Optimization Theory and Applications , vol. 147, no. 3, pp. 516–545, 2010
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D. P. Bertsekas, “Incremental proximal methods for large scale convex optimization,” Mathematical Programming , vol. 129, pp. 163–195, 2011
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G. Qu and N. Li, “Harnessing smoothness to accelerate distributed optimization,” in Decision and Control (CDC), 2016 IEEE 55th Conference on . IEEE, 2016, pp. 159–166
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A. Olshevsky, “Linear time average consensus and distributed optimization on fixed graphs,” SIAM Journal on Control and Optimization , vol. 55, no. 6, pp. 3990–4014, 2017
2017
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A. Chen and A. Ozdaglar, “A fast distributed proximal-gradient method,” in the 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) , 2012, pp. 601–608
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L. Gan, U. Topcu, and S. Low, “Optimal decentralized protocol for electric vehicle charging,” IEEE Transactions on Power Systems , vol. 28, no. 2, pp. 940–951, 2013
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M. Wang and D. P. Bertsekas, “Incremental constraint projection-proximal methods for nonsmooth convex optimization,” 2013, lab. for Information and Decision Systems Report LIDS-P-2907, MIT, July 2013
2013
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E. Wei and A. Ozdaglar, “On the O ( 1 / k ) {O}(1/k) convergence of asynchronous distributed alternating direction method of multipliers,” in Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE . IEEE, 2013, pp. 551–554
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A. Nedić and A. Olshevsky, “Distributed optimization over time-varying directed graphs,” in The 52nd IEEE Annual Conference on Decision and Control , 2013, pp. 6855–6860
2013
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K. Cai and H. Ishii, “Average consensus on arbitrary strongly connected digraphs with time-varying topologies,” IEEE Transactions on Automatic Control , vol. 59, no. 4, pp. 1066–1071, 2014
2014
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A. Nedić, A. Olshevsky, and C. A. Uribe, “Fast convergence rates for distributed non-bayesian learning,” IEEE Transactions on Automatic Control , vol. 62, no. 11, pp. 5538–5553, 2017
2017
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M. Hong and T.-H. Chang, “Stochastic proximal gradient consensus over random networks,” IEEE Transactions on Signal Processing , vol. 65, no. 11, pp. 2933–2948, 2017
2017
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A. Nedić, A. Olshevsky, and W. Shi, “Achieving geometric convergence for distributed optimization over time-varying graphs,” SIAM Journal on Optimization , vol. 27, no. 4, pp. 2597–2633, 2017
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A. Nedić, A. Olshevsky, W. Shi, and C. A. Uribe, “Geometrically convergent distributed optimization with uncoordinated step-sizes,” in American Control Conference (ACC), 2017 . IEEE, 2017, pp. 3950–3955
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J. Zeng and W. Yin, “ExtraPush for convex smooth decentralized optimization over directed networks,” Journal of Computational Mathematics, Special Issue on Compressed Sensing, Optimization, and Structured Solutions , vol. 35, no. 4, pp. 381–394, 2017
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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,” IEEE Transactions on Signal Processing , vol. 67, no. 3, pp. 708–723, 2017
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——, “Exact diffusion for distributed optimization and learning—part ii: Convergence analysis,” IEEE Transactions on Signal Processing , vol. 67, no. 3, pp. 724–739, 2017
2017
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G. Qu and N. Li, “Accelerated distributed nesterov gradient descent for convex and smooth functions,” in Decision and Control (CDC), 2017 IEEE 56th Annual Conference on . IEEE, 2017, pp. 2260–2267
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K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, and L. Massoulié, “Optimal algorithms for smooth and strongly convex distributed optimization in networks,” in International Conference on Machine Learning , 2017, pp. 3027–3036
2017
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2017
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T. Wu, K. Yuan, Q. Ling, W. Yin, and A. H. Sayed, “Decentralized consensus optimization with asynchrony and delays,” IEEE Transactions on Signal and Information Processing over Networks , vol. 4, no. 2, pp. 293–307, 2018
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M. Yan, “A new primal-dual algorithm for minimizing the sum of three functions with a linear operator,” Journal of Scientific Computing , p. to appear, 2018
2018
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J. Xu, S. Zhu, Y. Soh, and L. Xie, “Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,” in Proceedings of the 54th IEEE Conference on Decision and Control (CDC) , 2015, pp. 2055–2060
2060
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