A Rather General Class of Robust Optimization Problems with Conic Representable uncertainty set

The robust optimization methodology is a method dealing with uncertain optimization problems with hard constraints. We consider a rather genera class of programming problems with data uncertainty, where the uncertainty set is defined by conics. Our results unify a number of special cases that have been investigated in the literature and are applicable to a wider area of problems and more general uncertainty sets than those considered so far. The analysis in this paper makes it possible to use existing optimization algorithms to solve more complicated robust optimization problems

A Rather General Class of Robust Optimization Problems with Conic Representable uncertainty set Keywords:

Robust nonlinear optimization , Conic representable , Uncertain set , Robust counterpart