Details of Research Outputs

TitleStochastic proximal gradient consensus over time-varying networks
Author (Name in English or Pinyin)
Hong, M.1; Chang, T.-H.2
Date Issued2016-03-20
Conference Name2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source Publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference PlaceShanghai, China
DOI10.1109/ICASSP.2016.7472584
Indexed BySCOPUS
Funding Project国家自然科学基金项目
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
CN9781479999880
Published range国外学术期刊
Volume Issue Pages2016 March, 4776-4780
References
[1] G. B. Giannakis, Q. Ling, G. Mateos, I. D. Schizas, and H. Zhu, "Proximal splitting methods in signal processing, " in Splitting Methods in Communication and Imaging. Springer New York, 2015.
[2] A. Nedic and A. Ozdaglar, "Distributed subgradient methods for multiagent optimization, " IEEE Transactions on Automatic Control, vol. 54, no. 1, pp. 48-61, 2009.
[3] A. Nedic, A. Ozdaglar, and P. A. Parrilo, "Constrained consensus and optimization in multi-agent networks, " IEEE Transactions on Automatic Control, vol. 55, no. 4, pp. 922-938, April 2010.
[4] A. Nedic, A. Olshevsky, A. Ozdaglar, and J. N. Tsitsiklis, "Distributed subgradient methods and quantization effects, " in IEEE Conference on Decision and Control, Dec 2008, pp. 4177-4184.
[5] I. Chen, "Fast distributed first-order methods, " 2012, Master's thesis, Massachusetts Institute of Technology.
[6] W. Shi, Q. Ling, G. Wu, and W. Yin, "EXTRA: An exact first-order algorithm for decentralized consensus optimization, " 2014, online at arXiv: 1404. 6264.
[7] W. Shi, Q. Ling, G. Wu, andW. Yin, "A proximal gradient algorithm for decentralized nondifferentiable optimization, " in International Conference on Acoustics, Speech and Signal Processing, 2015.
[8] D. Jakovetic, J. Xavier, and J. M. F. Moura, "Fast distributed gradient methods, " IEEE Transactions on Automatic Control, vol. 59, no. 5, pp. 1131-1146, May 2014.
[9] J. C. Duchi, A. Agarwal, and M. J. Wainwright, "Dual averaging for distributed optimization: Convergence analysis and network scaling, " IEEE Transactions on Automatic Control, vol. 57, no. 3, pp. 592-606, March 2012.
[10] D. P. Bertsekas and J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Athena-Scientific, second edition, 1999.
[11] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers, " Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2011.
[12] R. Glowinski, Numerical methods for nonlinear variational problems, Springer-Verlag, New York, 1984.
[13] I. Schizas, A. Ribeiro, and G. Giannakis, "Consensus in ad hoc wsns with noisy links-part i: Distributed estimation of deterministic signals, " IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 350-364, 2008.
[14] E. Wei and A. Ozdaglar, "On the O(1/k) convergence of asynchronous distributed alternating direction method of multipliers, " 2013, Preprint, available at arXiv: 1307. 8254.
[15] W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, "On the linear convergence of the ADMM in decentralized consensus optimization, " IEEE Transactions on Signal Processing, vol. 62, pp. 1750-1761, 2014.
[16] J. F. C. Mota, J. M. F. Xavier, P. M. Q. Aguiar, and M. Puschel, "Dadmm: A communication-efficient distributed algorithm for separable optimization, " IEEE Transactions on Signal Processing, vol. 61, no. 10, pp. 2718-2723, May 2013.
[17] H. Zhu, A. Cano, and G. B. Giannakis, "Distributed consensus-based demodulation: algorithms and error analysis, " IEEE Transactions on Wireless Communications, vol. 9, no. 6, pp. 2044-2054, June 2010.
[18] T. Erseghe, D. Zennaro, E. Dall'Anese, and L. Vangelista, "Fast consensus by the alternating direction multipliers method, " IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5523-5537, Nov 2011.
[19] T.-H. Chang, M. Hong, and X. Wang, "Multi-agent distributed optimization via inexact consensus admm, " IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 482-497, Jan 2015.
[20] Q. Ling, W. Shi, G. Wu, and A. Ribeiro, "DLM: Decentralized linearized alternating direction method of multipliers, " IEEE Transactions on Signal Processing, vol. 63, no. 15, pp. 4051-4064, Aug 2015.
[21] M. Hong, Z.-Q. Luo, and M. Razaviyayn, "Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems, " 2014, submitted for publication.
[22] N. Parikh and S. Boyd, "Proximal algorithms, " Foundations and Trends in Optimization, vol. 1, no. 3, pp. 1-112, 2013.
[23] J. Eckstein, "Some saddle-function splitting methods for convex programming, " Optimization Methods and Software, vol. 4, no. 1, pp. 75-83, 1994.
[24] M. Hong and T.-H. Chang, "Stochastic proximal gradient consensus over time-varying networks, " 2015, Technical Report.
[25] X. Gao, B. Jiang, and S. Zhang, "On the information-adaptive variants of the admm: An iteration complexity perspective, " 2014, Preprint.
[26] Y. Ouyang, Y. Chen, G. Lan, and Jr. E. Pasiliao, "An accelerated linearized alternating direction method of multipliers, " SIAM Journal on Imaging Sciences, vol. 8, no. 1, pp. 644-681, 2015.
[27] M. E. Yildiz and A. Scaglione, "Coding with side information for rateconstrained consensus, " IEEE Trans. Signal Process., vol. 56, no. 8, pp. 3753-3764, 2008.
[28] S. Sundlhar Ram, A. Nedíc, and V. V. Veeravalli, "Distributed stochastic subgradeint projection algorithms for convex optimization, " J. Optim. Theory Appl., vol. 147, pp. 516-545, 2010.
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/679
CollectionSchool of Science and Engineering
Affiliation
1.Dept. of IMSE and ECE, Iowa State University, Ames, IA 50011, United States
2.School of Science and Engineering, Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China
Recommended Citation
GB/T 7714
Hong, M.,Chang, T.-H. Stochastic proximal gradient consensus over time-varying networks[C],2016.
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