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  1. No Access

    Chapter

    Simulation-Based Optimality Tests for Stochastic Programs

    Assessing whether a solution is optimal, or near-optimal, is fundamental in optimization. We describe a simple simulation-based procedure for assessing the quality of a candidate solution to a stochastic progr...

    Güzin Bayraksan, David P. Morton, Amit Partani in Stochastic Programming (2011)

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    Chapter

    Stochastic Constraints and Variance Reduction Techniques

    We provide an overview of two select topics in Monte Carlo simulation-based methods for stochastic optimization: problems with stochastic constraints and variance reduction techniques. While Monte Carlo simula...

    Tito Homem-de-Mello, Güzin Bayraksan in Handbook of Simulation Optimization (2015)

  3. No Access

    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)