Distributed dual averaging in networks

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Duchi, John C ^a   Agarwal, Alekh ^a   Wainwright, Martin J ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE RATES; CONVEX FUNCTIONS; DECENTRALIZED OPTIMIZATION; DUAL AVERAGING; GLOBAL OBJECTIVE; IN NETWORKS; LOCAL COMMUNICATIONS; LOCAL COMPUTATION; SHARP BOUNDS; SUBGRADIENT;

FUNCTIONS;

EID: 85162038689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation: numerical methods. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1989.
2. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah. Randomized gossip algorithms. IEEE Transactions on Information Theory, 52(6):2508-2530, 2006.
3. F.R.K. Chung. Spectral Graph Theory. AMS, 1998.
4. J. Duchi, A. Agarwal, and M. Wainwright. Dual averaging for distributed optimization: convergence analysis and network scaling. URL http://arxiv.org/ abs/1005.2012, 2010.
5. J. Friedman, J. Kahn, and E. Szemerédi. On the second eigenvalue of random regular graphs. In Proceedings of the Twenty First Annual ACM Symposium on Theory of Computing, pages 587-598, New York, NY, USA, 1989. ACM.
6. R. Gray. Toeplitz and circulant matrices: A review. Foundations and Trends in Communications and Information Theory, 2(3):155-239, 2006.
7. P. Gupta and P. R. Kumar. The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2):388-404, 2000.
8. J. Hiriart-Urruty and C. Lemaréchal. Convex Analysis and Minimization Algorithms I. Springer, 1996.
9. J. Hiriart-Urruty and C. Lemaréchal. Convex Analysis and Minimization Algorithms II. Springer, 1996.
10. R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, 1985.
11. B. Johansson, M. Rabi, and M. Johansson. A randomized incremental subgradient method for distributed optimization in networked systems. SIAM Journal on Optimization, 20(3):1157-1170, 2009.
12. A. Kalai and S. Vempala. Efficient algorithms for online decision problems. Journal of Computer and System Sciences, 71(3):291-307, 2005.
13. V. Lesser, C. Ortiz, and M. Tambe, editors. Distributed Sensor Networks: A Multiagent Perspective, volume 9. Kluwer Academic Publishers, May 2003.
14. D. Levin, Y. Peres, and E.Wilmer. Markov Chains and Mixing Times. American Mathematical Society, 2008.
15. I. Lobel and A. Ozdaglar. Distributed subgradient methods over random networks. Technical Report 2800, MIT LIDS, 2008.
16. R. McDonald, K. Hall, and G. Mann. Distributed training strategies for the structured perceptron. In North American Chapter of the Association for Computational Linguistics (NAACL), 2010.
17. A. Nedic and D. P. Bertsekas. Incremental subgradient methods for nondifferentiable optimization. SIAM Journal on Optimization, 12(1):109-138, 2001.
18. A. Nedic and A. Ozdaglar. Distributed subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control, 54:48-61, 2009.
19. A. Nemirovski and D. Yudin. Problem Complexity and Method Efficiency in Optimization. Wiley, New York, 1983.
20. Y. Nesterov. Primal-dual subgradient methods for convex problems. Mathematical Programming A, 120(1):261-283, 2009.
21. S. Sundhar Ram, A. Nedic, and V. V. Veeravalli. Distributed subgradient projection algorithm for convex optimization. In IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 3653-3656, 2009.
22. J. Tsitsiklis. Problems in decentralized decision making and computation. PhD thesis, Massachusetts Institute of Technology, 1984.
23. U. von Luxburg, A. Radl, and M. Hein. Hitting times, commute distances, and the spectral gap for large random geometric graphs. URL http://arxiv.org/ abs/1003.1266, 2010.
24. L. Xiao, S. Boyd, and S. J. Kim. Distributed average consensus with least-mean-square deviation. Journal of Parallel and Distributed Computing, 67(1):33-46, 2007.