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  1. No Access

    Article

    Residuals-based distributionally robust optimization with covariate information

    We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. ...

    Rohit Kannan, Güzin Bayraksan, James R. Luedtke in Mathematical Programming (2023)

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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)

  3. No Access

    Article

    Identifying effective scenarios in distributionally robust stochastic programs with total variation distance

    Traditional stochastic programs assume that the probability distribution of uncertainty is known. However, in practice, the probability distribution oftentimes is not known or cannot be accurately approximated...

    Hamed Rahimian, Güzin Bayraksan, Tito Homem-de-Mello in Mathematical Programming (2019)

  4. 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)

  5. No Access

    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)

  6. No Access

    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)

  7. 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)

  8. No Access

    Article

    Assessing solution quality in stochastic programs

    Determining whether a solution is of high quality (optimal or near optimal) is fundamental in optimization theory and algorithms. In this paper, we develop Monte Carlo sampling-based procedures for assessing s...

    Güzin Bayraksan, David P. Morton in Mathematical Programming (2006)