Decision-dependent distributionally robust standard quadratic optimization with Wasserstein ambiguity
This paper proposes a distributionally robust framework for standard quadratic optimization under Wasserstein ambiguity, demonstrating its equivalence to a modified deterministic StQP instance while providing out-of-sample performance guarantees and empirical validation.