Stochastic approximation for Monte Carlo optimization (1986)

Authors:
Peter W. Glynn
Affiliations:
University of Wisconsin-Madison, Madison, WI
Venue:
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Year:
2007

Citing 0
Cited 2

Sensitivity analysis and the “what if” problem in simulation analysis

Mathematical and Computer Modelling: An International Journal
Generalized estimates for performance sensitivities of stochastic systems

Mathematical and Computer Modelling: An International Journal

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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.