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We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function, the agents' sum-utility, plus a non-smooth (extended-valued) convex one.
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W. Shi, Q. Ling, G. Wu, and W. Yin, “EXTRA: An exact first-order algorithm for decentralized consensus optimization,” SIAM Journal on Optimization , vol. 25, no. 2, pp. 944–966, 2015
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P. Di Lorenzo and G. Scutari, “Next: In-network nonconvex optimization,” IEEE Transactions on Signal and Information Processing over Networks , vol. 2, no. 2, pp. 120–136, 2016
2016
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A. Nedich, A. Olshevsky, and W. Shi, “Achieving geometric convergence for distributed optimization over time-varying graphs,” SIAM J. on Optimization , vol. 27, no. 4, pp. 2597–2633, 2017
2017
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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 Proceedings of the 34th International Conference on Machine Learning , 2017, pp. 3027–3036
2017
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G. Qu and N. Li, “Harnessing smoothness to accelerate distributed optimization,” IEEE Transactions on Control of Network Systems , vol. 5, no. 3, pp. 1245–1260, 2017
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, 2018
2018
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D. Jakovetić, “A unification and generalization of exact distributed first-order methods,” IEEE Transactions on Signal and Information Processing over Networks , vol. 5, no. 1, pp. 31–46, 2018
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2018
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G. Scutari and Y. Sun, “Distributed nonconvex constrained optimization over time-varying digraphs,” Mathematical Programming , vol. 176, no. 1–2, pp. 497–544, July 2019
2019
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2019
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2019
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2019
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J. Xu, Y. Sun, Y. Tian, and G. Scutari, “A unified algorithmic framework for distributed composite optimization,” Purdue Technical Report , July 2019. [Online]. Available: arxiv preprint
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 , vol. 67, no. 17, pp. 4494–4506, 2019
2019
Cited alongside, same era.
A. Sundararajan, B. Hu, and L. Lessard, “Robust convergence analysis of distributed optimization algorithms,” in Proc. of the 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Cited in the paper.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,” in Proceedings of 54th IEEE Conference on Decision and Control (CDC) , 2015, pp. 2055–2060
2060
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