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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-...
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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...