robustify is used to derive a robust counterpart of an uncertain optimization problem (constraints satisfied for all possible uncertainties, and worst-case objective).
Syntax
[Frobust,robustobjective] = robustify(F,objective,options)
Examples
Consider the following uncertain problem (w is the uncertain variable)
sdpvar x w
F = [x+w <= 1];
W = [uncertain(w),-0.5 <= w <= 0.5];
objective = -x;
F = [x+w <= 1];
W = [uncertain(w),-0.5 <= w <= 0.5];
objective = -x;
The problem can be solved immediately
solvesdp([F, W],objective);
Alternatively, we can first derive a robust version.
[Frobust,robustobjective] = robustify(F + G,objective);
This model does not involve the uncertain variable anymore, and corresponds to the worst-case scenario model. We can now solve the model.
solvesdp(Frobust,robustobjective,ops)
See also
solvesdp, uncertain, sdpsettings, robust optimization tutorial, robust MPC example