sparse uncertainty

Classical robust optimization relies on convex and bounded uncertainty sets, an assumption that is inadequate for sparse uncertainty, where only a small, unknown subset of parameters deviates from its nominal value. This sparsity makes the uncertainty set nonconvex and turns separation into a combinatorial problem, so standard duality-based reformulations do not apply. We study two … Read more