Daniel de Vicente

Distributionally robust chance-constrained standard quadratic optimization with Wasserstein ambiguity

The standard quadratic optimization problem consists of minimizing a quadratic form over the standard simplex. It has highly relevant applications ranging from portfolio optimization to machine learning. However, if the quadratic form is neither convex nor concave, this problem is NP-hard. We study a distributionally r...