The Dao of Robustness
We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution. Unlike data-driven robust optimization approaches, the decision maker does not have to size the ambiguity set, but […]