Alexander Shapiro, Georgia Institute of Technology, Atlanta
(SPEPS)
Society for Industrial and Applied Mathematics - SIAM (Philadelphia, Pa.)
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| edoc-Server der Humboldt-Universität zu Berlin |
| Author(s): |
Anton J. Kleywegt, Georgia Institute of Technology, Atlanta Alexander Shapiro, Georgia Institute of Technology, Atlanta | Title: | The Sample Average Approximation Method for Stochastic Discrete Optimization |
| Date of Acceptance: | 29.11.1999 |
| Submission Date: | 02.11.1999 |
| Series Title: |
Stochastic Programming E-Print Series (SPEPS) |
| Editors: | Julie L. Higle; Werner Römisch; Surrajeet Sen |
| Keywords (eng): | stochastic programming, discrete optimization, Monte Carlo sampling, law of large numbers, large deviations theory, sample average approximation, stopping rules, stochastic knapsack problem |
| Appeared in: |
SIAM Journal on Optimization 2.2000 (Vol. 12)
Society for Industrial and Applied Mathematics - SIAM (Philadelphia, Pa.) |
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To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.
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Endnote Bibtex |
| Abstract (eng): | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In this paper 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 consequently 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 and stopping rules of this procedure and present a numerical example of the stochastic knapsack problem. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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