Stochastic approximation for Monte Carlo optimization

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Stochastic approximation for Monte Carlo optimization

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Winter Simulation Conference archive
Proceedings of the 18th conference on Winter simulation table of contents

Washington, D.C., United States

Pages: 356 - 365

Year of Publication: 1986

ISBN:0-911801-11-1

Author

Peter W. Glynn

Department of Industrial Elngineering and Mathematics Research Center, University of Wisconsin--Madison, Madison, WI

Sponsor

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 12, Downloads (12 Months): 61, Citation Count: 20

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ABSTRACT

In this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to regenerative processes, operates by estimating finite differences. The second method is of Robbins-Monro type and is applicable to Markov chains. The algorithm is driven by derivative estimates obtained via a likelihood ratio argument.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Chung, K. L. (1974). A Course in Probability The~. Academic Press, New York.
	2	Metivier, M. and Priouret, P. (1984). Applications of a Kushner and Clark lemma to general classes of stochastic algorithms. IEEE Trans. Inform. Theory 30, 140-151.
	3	Smith, W. L. (1955). Regenerative stochastic processes. Proc. Roy. Soc. A232, 6-31.

CITED BY 20

	Marc S. Meketon, Optimization in simulation: a survey of recent results, Proceedings of the 19th conference on Winter simulation, p.58-67, December 14-16, 1987, Atlanta, Georgia, United States
	Peter W. Glynn, Likelilood ratio gradient estimation: an overview, Proceedings of the 19th conference on Winter simulation, p.366-375, December 14-16, 1987, Atlanta, Georgia, United States
	James R. Wilson, Future directions in response surface methodology for simulation, Proceedings of the 19th conference on Winter simulation, p.378-381, December 14-16, 1987, Atlanta, Georgia, United States
	Peter W. Glynn, Optimization of stochastic systems, Proceedings of the 18th conference on Winter simulation, p.52-59, December 08-10, 1986, Washington, D.C., United States
	Sigrún Andradóttir, Discrete optimization in simulation: a method and applications, Proceedings of the 24th conference on Winter simulation, p.483-486, December 13-16, 1992, Arlington, Virginia, United States
	Marvin K. Nakayama, A characterization of the simple failure-biasing method for simulations of highly reliable Markovian Systems, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.4 n.1, p.52-88, Jan. 1994
	Sigrún Andradóttir, A review of simulation optimization techniques, Proceedings of the 30th conference on Winter simulation, p.151-158, December 13-16, 1998, Washington, D.C., United States
	Pierre L'Ecuyer, An overview of derivative estimation, Proceedings of the 23rd conference on Winter simulation, p.207-217, December 08-11, 1991, Phoenix, Arizona, United States
	Enrique Campos-Náñez , Stephen D. Patek, On improving the performance of simulation-based algorithms for average reward processes with application to network pricing, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
	Haïscam Abdallah , Moulaye Hamza, On the sensitivity analysis of the expected accumulated reward, Performance Evaluation, v.47 n.2, p.163-179, February 2002
	Farhad Azadivar, A tutorial on simulation optimization, Proceedings of the 24th conference on Winter simulation, p.198-204, December 13-16, 1992, Arlington, Virginia, United States
	Peter W. Glynn, Likelihood ratio gradient estimation for stochastic systems, Communications of the ACM, v.33 n.10, p.75-84, Oct. 1990
	Michael Devetsikiotis , J. Keith Townsend, Statistical optimization of dynamic importance sampling parameters for efficient simulation of communication networks, IEEE/ACM Transactions on Networking (TON), v.1 n.3, p.293-305, June 1993
	Ming-hua Hsieh , Peter W. Glynn, Recent advances in simulation optimization: confidence regions for stochastic approximation algorithms, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	Ming-hua Hsieh, Adaptive monte carlo methods for rare event simulation: adaptive monte carlo methods for rare event simulations, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
	Farhad Azadivar, Simulation optimization methodologies, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.93-100, December 05-08, 1999, Phoenix, Arizona, United States
	Peter Marbach , John N. Tsitsiklis, Approximate Gradient Methods in Policy-Space Optimization of Markov Reward Processes, Discrete Event Dynamic Systems, v.13 n.1-2, p.111-148, January-April 2003
	Mohamed A. Ahmed , Talal M. Alkhamis, Sensitivity analysis for transient single server queuing models using an interpolation approach, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
	Jonathan Baxter , Peter L. Bartlett, Infinite-horizon policy-gradient estimation, Journal of Artificial Intelligence Research, v.15 n.1, p.319-350, July 2001
	Jonathan Baxter , Peter L. Bartlett , Lex Weaver, Experiments with infinite-horizon, policy-gradient estimation, Journal of Artificial Intelligence Research, v.15 n.1, p.351-381, July 2001