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    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)

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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)

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    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)

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    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)