Articlegreen2002

The Sample Average Approximation Method for Stochastic Discrete Optimization

Anton J. Kleywegt · Alexander Shapiro · Tito Homem‐de‐Mello

1,989 citations0/0 files storedSIAM Journal on Optimization

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Simulation Techniques and Applications Risk and Portfolio Optimization Stochastic Gradient Optimization Techniques

Abstract

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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References

2011Introduction to Stochastic Programming

Cited by

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