Confidence Regions in Wasserstein Distributionally Robust Estimation

Wasserstein distributionally robust optimization (DRO) estimators are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance (in a Wasserstein sense) from the underlying empirical measure. While motivated by the need to identify model parameters (or) decision choices that are … Read more

Asymptotic Behaviour of the Quadratic Knapsack Problem

We study subclasses of the quadratic knapsack problem, where the profits are independent random variables defined on the interval [0,1] and the knapsack capacity is proportional to the number of items (we assume that the weights are arbitrary numbers from the interval [0,1]). We show asymptotically that the objective value of a very easy heuristic … Read more

Asymptotic Convergence Analysis for Distributional Robust Optimization and Equilibrium Problems

In this paper, we study distributional robust optimization approaches for a one stage stochastic minimization problem, where the true distribution of the underlying random variables is unknown but it is possible to construct a set of probability distributions which contains the true distribution and optimal decision is taken on the basis of worst possible distribution … Read more

Existence and stability results based on asymptotic analysis for semidefinite linear complementarity problems

This work is devoted to the study of existence and stability results of semidefinite linear complementarity problems (SDLCP). Our approach consists of approximating the variational inequality formulation of the SDLCP by a sequence of suitable chosen variational inequalities. This provides particular estimates for the asymptotic cone of the solution set of the SDLCP. We thus … Read more