Robust Optimization (RO) arises in two
stages of optimization, first level for maximizing over the uncertain data and
second level for minimizing over the feasible set. It is the most suitable
mathematical optimization procedure to solve real-life problem models. In the
present work, we characterize robust solutions for both homogeneous and
non-homogeneous quadratically constrained quadratic optimization problem where
constraint function and cost function are uncertain. Moreover, we discuss about
optimistic dual and strong robust duality of the considered uncertain quadratic
optimization problem. Finally, we complete this work with an example to
illustrate our solution method.
Classification: (2010) 90C20 -
90C26 - 90C46-90C47
Keywords: Robust Optimization, Data Uncertainty, Quadratic
Optimization Strong Duality, Robust Solution, DPJ-Convex.