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Stochastic approximation for Monte Carlo optimization (1986)

Published: 09 December 2007 Publication History

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

[1]
Chung, K. L. (1974). A Course in Probability Theory. 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.

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Published In

cover image ACM Conferences
WSC '07: Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
December 2007
2659 pages
ISBN:1424413060

Sponsors

  • IIE: Institute of Industrial Engineers
  • INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
  • ASA: American Statistical Association
  • IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
  • SIGSIM: ACM Special Interest Group on Simulation and Modeling
  • NIST: National Institute of Standards and Technology
  • (SCS): The Society for Modeling and Simulation International

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IEEE Press

Publication History

Published: 09 December 2007

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  • Research-article

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WSC07
Sponsor:
  • IIE
  • INFORMS-SIM
  • ASA
  • IEEE/SMC
  • SIGSIM
  • NIST
  • (SCS)
WSC07: Winter Simulation Conference
December 9 - 12, 2007
Washington D.C.

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WSC '07 Paper Acceptance Rate 152 of 244 submissions, 62%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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