Once you have installed robust, and have a GP or SP model, you are ready to begin. From here onward, we will use nominal to describe models with no uncertainty straight out of GPkit, and robust to describe models that have been robustified using robust.
The uncertainties in robust are defined by adding attribute pr to any variable in your model. This attribute describes the 3\sigma uncertainty for the given parameter, normalized by its mean (otherwise known as 3 times the coefficient of variation). Note that these attributes are carried by nominal models but only come into effect when robust is applied.
from gpkit import Variable, Model
x = Variable('x', pr = 12) # 3CV = 12%
# ...
# after more variables, constraints
# ...
m = Model(objective, constraints, substitutions)Once you have added uncertainties to parameters, and created a GPkit model, robustifying said model and solving it is easy. The most straight-forward inputs for uncertainty_set are 'box' or 'elliptical'. gamma defines the size of the uncertainty set protected against, where gamma=1 protects against 3\sigma uncertainty.
from robust.robust import RobustModel
rm = RobustModel(m, uncertainty_set, gamma = float)
rsol = rm.robustsolve()You have solved your robust model! To be able to quickly compare the robust solution rsol with the nominal solution sol, we recommend you try 'diffing' the two, which is done as follows:
print rsol.diff(sol)This will allow you to see the percent differences between the two designs! Since the robust design protects against uncertainty in the parameters, it will necessarily have lower performance than the nominal design. If this has piqued your interest, please continue to explore the documentation.