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Constructing Data-Driven Budgeted Uncertainty Sets for Robust Optimization
In robust optimization, budgeted uncertainty sets offer a popular trade-off between modeling flexibility and computational tractability. Data that is observed in practice is often discrete in nature, which creates the need for tools to construct budgeted uncertainty sets based on observed data. This
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