Asynchronous gossip algorithms for stochastic optimization

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2009, Pages 3581-3586

  Ram, S Sundhar ^a   Nedic A ^a   Veeravalli, V V ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLOCKS; FUNCTIONS; MULTI AGENT SYSTEMS; OPTIMIZATION; STOCHASTIC SYSTEMS;

ASYNCHRONOUS ALGORITHMS; CONVERGENCE PROPERTIES; DIFFERENTIABLE FUNCTIONS; GOSSIP ALGORITHMS; MULTIAGENT NETWORKS; OBJECTIVE FUNCTIONS; STOCHASTIC ERRORS; STOCHASTIC OPTIMIZATIONS;

SOFTWARE AGENTS;

EID: 77950803166     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2009.5399485     Document Type: Conference Paper

References (32)

1. T. Aysal, M. Yildiz, A. Sarwate, and A. Scaglione, Broadcast gossip algorithms: Design and analysis for consensus, Proceedings of the 47th IEEE Conference on Decision and Control, 2008.
2. D. P. Bertsekas and J. N. Tsitsiklis, Parallel and distributed computation: Numerical methods, Athena Scientific, 1997.
3. -, Gradient convergence in gradient methods with errors, SIAM Journal of Optimization 10 (2000), no.3, 627-642.
4. D. Blatt, A. O. Hero, and H. Gauchman, A convergent incremental gradient method with constant stepsize, SIAM Journal of Optimization 18 (2007), no.1, 29-51.
5. V. Borkar, Asynchronous stochastic approximations, SIAM Journal on Control and Optimization 36 (1998), no.3, 840-851.
6. -, Stochastic approximation: A dynamical viewpoint, Cambridge University Press, 2008.
7. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms, IEEE Transactions on Information Theory 52 (2006), no.6, 2508-2530.
8. A. Dimakis, A. Sarwate, and M. Wainwright, Geographic gossip: Efficient averaging for sensor networks, IEEE Transactions on Signal Processing 56 (2008), no.3, 1205-1216.
9. R. Dudley, Real analysis and probability, Cambridge University Press, 2002.
10. Y. Ermoliev, Stochastic programming methods, Nauka, Moscow, 1976.
11. -, Stochastic quasi-gradient methods and their application to system optimization, Stochastics 9 (1983), no.1, 1-36.
12. A. A. Gaivoronski, Convergence properties of backpropogation for neural nets via theory of stochastic gradient methods. Part 1., Optimization Methods and Software 4 (1994), no.2, 117-134.
13. R. G.Gallager, Discrete stochastic processes, Kluwer Academic Publishers, Norwell, Massachusetts, USA, 1996.
14. B. Johansson, On distributed optimization in networked systems, Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden, 2008.
15. B. Johansson, M. Rabi, and M. Johansson, A simple peer-to-peer algorithm for distributed optimization in sensor networks, Proceedings of the 46th IEEE Conference on Decision and Control, 2007, pp. 4705-4710.
16. B. Johanssson, T. Keviczsky, M. Johansson, and K. Johansson, Subgradient methods and consensus algorithms for solving convex optimization problems, Proceedings of the 47th IEEE Conference on Decision and Control, 2008, pp. 4185-4190.
17. K. C. Kiwiel, Convergence of approximate and incremental subgradient methods for convex optimization, SIAM Journal on Optimization 14 (2003), no.3, 807-840.
18. I. Lobel and A. Ozdaglar, Distributed subgradient methods over random networks, Lab. for Information and Decision Systems, MIT, Report 2800, 2008.
19. A. Nedić and D. P. Bertsekas, Incremental subgradient method for nondifferentiable optimization, SIAM Journal of Optimization 12 (2001), 109-138.
20. -, The effect of deterministic noise in sub-gradient methods, Tech. report, Lab. for Information and Decision Systems, MIT, 2007.
21. A. Nedić, A. Olshevsky, A. Ozdaglar, and J. N. Tsitsiklis, Distributed subgradient algorithms and quantization effects, Proceedings of the 47th IEEE Conference on Decision and Control, 2008.
22. A. Nedić and A. Ozdaglar, On the rate of convergence of distributed asynchronous subgradient methods for multi-agent optimization, Proceedings of the 46th IEEE Conference on Decision and Control, 2007, pp. 4711-4716.
23. B. T. Polyak, Introduction to optimization, Optimization Software Inc., 1987.
24. M. G. Rabbat and R. D. Nowak, Quantized incremental algorithms for distributed optimization, IEEE Journal on Select Areas in Communications 23 (2005), no.4, 798-808.
25. S. Sundhar Ram, A. Nedić, and V. V. Veeravalli, Distributed stochastic subgradient algorithm for convex optimization, Available at http://arxiv.org/abs/0811.2595, 2008.
26. S. Sundhar Ram, A. Nedić, and V. V. Veeravalli, Incremental stochastic sub-gradient algorithms for convex optimization, Available at http://arxiv.org/abs/0806.1092, 2008.
27. S. Sundhar Ram, V. V. Veeravali, and A. Nedić, Sensor networks: When theory meets practice, ch. Distributed and recursive estimation, Springer, 2009.
28. S. Sundhar Ram, V. V. Veeravalli, and A. Nedić, Distributed and nonautonomous power control through distributed convex optimization, IEEE INFOCOM, 2009.
29. M. V. Solodov, Incremental gradient algorithms with stepsizes bounded away from zero, Computational Optimization and Algorithms 11 (1998), no.1, 23-35.
30. M. V. Solodov and S. K. Zavriev, Error stability properties of generalized gradient-type algorithms, Journal of Optimization Theory and Applications 98 (1998), no.3, 663-680.
31. J. N. Tsitsiklis, Problems in decentralized decision making and computation, Ph.D. thesis, Massachusetts Institute of Technology, 1984.
32. J. N. Tsitsiklis, D. P. Bertsekas, and M. Athans, Distributed asynchronous deterministic and stochastic gradient optimization algorithms, IEEE Transactions on Automatic Control 31 (1986), no.9, 803-812.