Optimization under Uncertainty: Bounding the Correlation Gap a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

@inproceedings{Agrawal2011OptimizationUU,
  title={Optimization under Uncertainty: Bounding the Correlation Gap a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy},
  author={Shipra Agrawal},
  year={2011}
}
Preface Modern decision models increasingly involve parameters that are unknown or uncertain. Uncertainty is typically modeled by probability distribution over possible realizations of some random parameters. In presence of high dimensional multivari-ate random variables, estimating the joint probability distributions is difficult, and optimization models are often simplified by assuming that the random variables are independent. Although popular, the effect of this heuristic on the solution… CONTINUE READING

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Robustness against dependence in PERT: An application of duality and distributions with known marginals

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