In this paper we focus on robust linear optimization problems with uncertainty regions defined by ø-divergences (for example, chi-squared, Hellinger, Kullback-Leibler). We show how uncertainty regions based on ø-divergences arise in a natural way as confidence sets if the uncertain parameters...

Tucker's well-known combinatorial lemma states that for any given symmetric triangulation of the n-dimensional unit cube and for any integer labeling that assigns to each vertex of the triangulation a label from the set {+/-1,+/-2,...,+/-n} with the property that antipodal vertices on the...

A robust price driven co-ordination algorithm for distributed, worst case linear quadratic optimization problems is presented. The approach is to rewrite the dual problem associated with the centralized robust optimization problem and to modify the co-ordination scheme in order to incorporate...

Robust optimization is a methodology that can be applied to problems that are affected by uncertainty in the problem's parameters. The classical robust counterpart (RC) of the problem requires the solution to be feasible for all uncertain parameter values in a so-called uncertainty set, and...