Fetching the paper…
Reading the bibliography…
We study distributed composite optimization over networks: agents minimize a sum of smooth (strongly) convex functions, the agents' sum-utility, plus a nonsmooth (extended-valued) convex one.
2002
Earlier work this paper cites.
L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” Systems & Control Letters , vol. 53, no. 1, pp. 65–78, 2004
2004
Earlier work this paper cites.
A. Agarwal, S. Negahban, and M. J. Wainwright, “Fast global convergence rates of gradient methods for high-dimensional statistical recovery,” in Proceedings of Advances in Neural Information Processing Systems , 2010, pp. 37–45
2010
Earlier work this paper cites.
A. Wien, Iterative solution of large linear systems . Lecture Notes, TU Wien, 2011
2011
Earlier work this paper cites.
E. Wei and A. E. Ozdaglar, “Distributed alternating direction method of multipliers,” in Proceedings of IEEE 51st Annual Conference on Decision and Control , 2012, pp. 5445–5450
2012
Earlier work this paper cites.
R. A. Horn and C. R. Johnson, Matrix Analysis . Cambridge University Press, 2012
2012
Earlier work this paper cites.
O. Shamir, N. Srebro, and T. Zhang, “Communication-efficient distributed optimization using an approximate newton-type method,” in Proceedings of International Conference on Machine Learning , 2014, pp. 1000–1008
2014
Earlier work this paper cites.
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
2015
Earlier work this paper cites.
W. Shi, Q. Ling, G. Wu, and W. Yin, “A proximal gradient algorithm for decentralized composite optimization,” IEEE Transactions on Signal Processing , vol. 63, no. 22, pp. 6013–6023, Nov 2015
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
A. Nedich, 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
2017
Earlier work this paper cites.
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
Cited alongside, same era.
N. S. Aybat, Z. Wang, T. Lin, and S. Ma, “Distributed linearized alternating direction method of multipliers for composite convex consensus optimization,” IEEE Transactions on Automatic Control , vol. 63, no. 1, pp. 5–20, 2017
2017
Cited alongside, same era.
A. Sundararajan, B. Hu, and L. Lessard, “Robust convergence analysis of distributed optimization algorithms,” in Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing , 2017, pp. 2740–2749
2017
Cited alongside, same era.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Convergence of asynchronous distributed gradient methods over stochastic networks,” IEEE Transactions on Automatic Control , vol. 63, no. 2, pp. 434–448, 2017
2017
J. Xu, Y. Sun, Y. Tian, and G. Scutari, “A unified contraction analysis of a class of distributed algorithms for composite optimization,” IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
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
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
2017
Cited alongside, same era.
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
Cited alongside, same era.
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
2018
Cited alongside, same era.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “A Bregman splitting scheme for distributed optimization over networks,” IEEE Transactions on Automatic Control , vol. 63, no. 11, pp. 3809–3824, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
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 , no. 3, pp. 724–739, 2018
2018
Cited alongside, same era.
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
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Accelerated primal-dual algorithms for distributed smooth convex optimization over networks,” in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) , 2020
2020
Closest in time.
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 the 54th IEEE Conference on Decision and Control , 2015, pp. 2055–2060
2060
Closest in time.