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A two Timescale Stochastic Approximation Scheme for Simulation-Based Parametric Optimization

Published online by Cambridge University Press:  27 July 2009

Shalabh Bhatnagar  and
Vivek S. Borkar
Shalabh Bhatnagar
Affiliation:
Institute for Systems Research, University of Maryland, College Park, MD [email protected]
Vivek S. Borkar
Affiliation:
Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560 012, [email protected]
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Abstract

A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation-based parametric optimization as an alternative to traditional “infinitesimal perturbation analysis” schemes. It avoids the aggregation of data present in many other schemes. Its convergence is analyzed, and a queueing example is presented.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 12 , Issue 4 , October 1998 , pp. 519 - 531
Copyright
Copyright © Cambridge University Press 1998

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References

1.Bhatnagar, S. & Borkar, V.S. (1997). Multiscale stochastic approximation for parametric optimization of hidden Markov models. Probability in the Engineering and Informational Sciences 11: 509–522.CrossRefGoogle Scholar
2.Borkar, V.S. (1995). Probability theory: An advanced course. New York: Springer Verlag.CrossRefGoogle Scholar
3.Borkar, V.S. (1997). Stochastic approximation with two time scales. Systems and Control Letters 29: 291–294.CrossRefGoogle Scholar
4.Chong, E.K.P. & Ramadge, R.J. (1994). Stochastic optimization of regenerative systems using infinitesimal perturbation analysis. IEEE Transactions on Automatic Control 39(7): 1400–1410.CrossRefGoogle Scholar
5.Chong, E.K.P. & Ramadge, P.J. (1993). Optimization of queues using an infinitesimal perturbation analysis-based stochastic algorithm with general update times. SIAM Journal on Control and Optimization 31(3): 698–732.CrossRefGoogle Scholar
6.Hirsch, M.W. (1987). Convergent activation dynamics in continuous time networks. Neural Networks 2: 331–349.CrossRefGoogle Scholar
7.Kushner, H.J. & Clark, D.S. (1978). Stochastic approximation methods for constrained and unconstrained systems. New York: Springer Verlag.CrossRefGoogle Scholar
8.L'Ecuyer, P. & Glynn, P.W. (1994). Stochastic optimization by simulation: Convergence proofs for the GI/G/I queue in steady state. Management Science 40(11): 1562–1578.CrossRefGoogle Scholar
9.Loeve, M. (1977). Probability theory 2, 4th ed.New York; Springer.Google Scholar
10.Rubinstein, R. (1981). Simulation and the Monte Carlo Method. New York: John Wiley.CrossRefGoogle Scholar
11.Ruszczynski, A. & Syski, W. (1983). Stochastic approximation method with gradient averaging for unconstrained problems. IEEE Transactions on Automatic Control AC-28(12): 1097–1105.CrossRefGoogle Scholar
12.Spall, J.C. (1992). Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control 37(3): 332–341.CrossRefGoogle Scholar
13.Suri, R. & Zazanis, M.A. (1988). Perturbation analysis gives strongly consistent estimates for the M/G/1 queue. Management Science 34(1): 39–64.CrossRefGoogle Scholar