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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...

    Jangho Park, Rebecca Stockbridge, Güzin Bayraksan in Annals of Operations Research (2021)

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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...

    Rebecca Stockbridge, Güzin Bayraksan in Computational Optimization and Applications (2016)

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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-...

    Rebecca Stockbridge, Güzin Bayraksan in Mathematical Programming (2013)