  Pareto efficiency for robust linear programs was introduced by Iancu and
Trichakis in [9]. We generalize their approach and theoretical results to
robust optimization problems in Euclidean spaces with affine uncertainty.
Additionally, we demonstrate the value of this approach in an exemplary manner
in the area of robust semidefinite programming (SDP). In particular, we prove
that computing a Pareto robustly optimal solution for a robust SDP is tractable
and illustrate the benefit of such solutions at the example of the maximal
eigenvalue problem. Furthermore, we modify the famous algorithm of Goemans and
Williamson [8] in order to compute cuts for the robust max-cut problem that
yield an improved approximation guarantee in non-worst-case scenarios.
