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Article
Variance reduction for sequential sampling in stochastic programming
This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming and presents a comparative comp...
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Article
Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming
This paper presents a comparative computational study of the variance reduction techniques antithetic variates and Latin hypercube sampling when used for assessing solution quality in stochastic programming. T...
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Article
A probability metrics approach for reducing the bias of optimality gap estimators in two-stage stochastic linear programming
Monte Carlo sampling-based estimators of optimality gaps for stochastic programs are known to be biased. When bias is a prominent factor, estimates of optimality gaps tend to be large on average even for high-...