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The Sample Average Approximation Method for Stochastic Discrete Optimization
Open accessAbstract
In thispaper we study a Monte Carlo simulation--based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates, stopping rules, and computational complexity of this procedure and present a numerical example for the stochastic knapsack problem.
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Cites
Multiple Comparison Procedures
Hochberg, Y., & Tamhane, A. C. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316672 · Cited by 2,389
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Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016) · IIE Transactions · doi:10.1080/0740817x.2015.1067735 · Cited by 854
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Metadata
- Authors
- Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T.
- Venue
- SIAM Journal on Optimization
- Published
- 2002-01
- Volume
- 12
- Issue
- 2
- Pages
- 479-502
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W1983916623
- Cited by
- 1,983
- References
- 1
- FWCI
- 28.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The Sample Average Approximation Method for Stochastic Discrete Optimization. SIAM Journal on Optimization. https://doi.org/10.1137/s1052623499363220
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