This section will help you understand the basic ideas behind robust optimization (RO).
RO is a tractable method for optimization under uncertainty, and specifically under uncertain parameters. It optimizes the worst-case objective outcome over uncertainty sets, unlike general stochastic optimization methods which optimize statistics of the distribution of the objective over probability distributions of uncertain parameters. As such, RO sacrifices generality for tractability, probabilistic guarantees and engineering intuition.
[paraphrased from Ozturk and Saab, 2019]
Given a general optimization problem under parametric uncertainty, we define the set of possible realizations of uncertain vector of parameters u in the uncertainty set \mathcal{U}. This allows us to define the problem under uncertainty below.
\text{min} &~~f_0(x) \\
\text{s.t.} &~~f_i(x,u) \leq 0,~\forall u \in \mathcal{U},~i = 1,\ldots,n
This problem is infinite-dimensional, since it is possible to formulate an infinite number of constraints with the countably infinite number of possible realizations of u \in \mathcal{U}. To circumvent this issue, we can define the following robust formulation of the uncertain problem below.
\text{min} &~~f_0(x) \\
\text{s.t.} &~~\underset{u \in \mathcal{U}}{\text{max}}~f_i(x,u) \leq 0,~i = 1,\ldots,n
This formulation hedges against the worst-case realization of the uncertainty in the defined uncertainty set. The set is often described by a norm, which contains possible uncertain outcomes from distributions with bounded support
\begin{split}
\text{min} &~~f_0(x) \\
\text{s.t.} &~~\underset{u}{\text{max}}~f_i(x,u) \leq 0,~i = 1,\ldots,n \\
&~~\left\lVert u \right\rVert \leq \Gamma \\
\end{split}
where \Gamma is defined by the user as a global uncertainty bound. The larger the \Gamma, the greater the size of the uncertainty set that is protected against.
Work in progress...