The sample average approximation method for stochastic discrete optimization

SIAM Journal on Optimization

Volumn 12, Issue 2, 2002, Pages 479-502

  Kleywegt, Anton J ^a   Shapiro, Alexander ^a   Homem De Mello, Tito ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Discrete optimization; Large deviations theory; Law of large numbers; Monte Carlo sampling; Sample average approximation; Stochastic knapsack problem; Stochastic programming; Stopping rules

Indexed keywords

EID: 0036013019     PISSN: 10526234     EISSN: None     Source Type: Journal    
DOI: 10.1137/S1052623499363220     Document Type: Article

References (22)

1. A simulated annealing algorithm with constant temperature for discrete stochastic optimization
2. Introduction to Stochastic Programming
3. The stochastic knapsack problem with random weights: A heuristic approach to robust transportation planning
4. Prababilistic search with overrides
5. Confidence sets for discrete stochastic optimization
6. Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms
7. Simulated annealing with noisy or imprecise energy measurements
8. Simulated annealing for noisy cost functious
9. Algorithm AS66: The normal integral
10. Monte Carlo methods for discrete stochastic optimization
11. Monte Carlo bounding techniques for determining solution quality in Stochastic programs
12. On a stochastic knapsack problem and generalizations
13. Simple procedures for selecting the best simulated system when the number of alternatives is large
14. On optimal allocation of indivisibles under uncertainty
15. A branch and bound method for stochastic global optimization
16. Solving stochastic programs with integer recourse by enumeration: A framework using Gröbner basis reductions
17. Asymptotic analysis of stochastic programs