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Monte Carlo simulation approach to stochastic programming

Published:09 December 2001Publication History

ABSTRACT

Various stochastic programming problems can be formulated as problems of optimization of an expected value function. Quite often the corresponding expectation function cannot be computed exactly and should be approximated, say by Monte Carlo sampling methods. In fact, in many practical applications, Monte Carlo simulation is the only reasonable way of estimating the expectation function. In this talk we discuss converges properties of the sample average approximation (SAA) approach to stochastic programming. We argue that the SAA method is easily implementable and can be surprisingly efficient for some classes of stochastic programming problems.

References

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  1. Monte Carlo simulation approach to stochastic programming

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              • Published in

                cover image ACM Conferences
                WSC '01: Proceedings of the 33nd conference on Winter simulation
                December 2001
                1595 pages
                ISBN:078037309X
                • Conference Chair:
                • Matt Rohrer,
                • Program Chair:
                • Deb Medeiros,
                • Publications Chair:
                • Mark Grabau

                Publisher

                IEEE Computer Society

                United States

                Publication History

                • Published: 9 December 2001

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                Acceptance Rates

                WSC '01 Paper Acceptance Rate111of155submissions,72%Overall Acceptance Rate3,413of5,075submissions,67%

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