UncertaintySets
Uncertainty sets are mathematical representations of the possible values that uncertain parameters may take in optimization and decision problems. They encode available information, such as historical data, expert judgment, or statistical bounds, and serve as the foundation for robust decision making. In robust optimization, decisions are chosen to perform well for all realizations of the uncertain parameters within the specified set.
Common uncertainty set shapes include boxes (intervals for each parameter), ellipsoids, and polyhedra. Box sets are
The robust counterpart of an optimization problem typically replaces uncertain coefficients by their worst-case values over
Uncertainty sets are frequently data-driven, calibrated from historical observations, confidence bounds, or estimated variances. They appear