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[Documentation work in progress...]

robust has a variety of tools beyond the basics to aid engineering design under uncertainty.

All of these methods have been implemented in the robustSPpaper code repository, which defines the models used in [Ozturk, 2019].

Choosing between RGP approximation methods and uncertainty sets

There are many possible **options to RobustModel, but the primary function of the options is to be able to choose between the different RGP approximation methods, which have differing levels of conservativeness. We prefer to define the three methods in a dict for ease access, and call RobustModel, for example for Best Pairs, as follows:

methods = [{'name': 'Best Pairs', 'twoTerm': True, 'boyd': False, 'simpleModel': False},
           {'name': 'Linearized Perturbations', 'twoTerm': False, 'boyd': False, 'simpleModel': False},
           {'name': 'Simple Conservative', 'twoTerm': False, 'boyd': False, 'simpleModel': True}]
method = methods[0] # Best Pairs
robust_model = RobustModel(m, uncertainty_set, gamma=Gamma, twoTerm=method['twoTerm'],
                               boyd=method['boyd'], simpleModel=method['simpleModel'])

For uncertainty_set, 'box' and 'elliptical' are currently supported, and define the \infty- and 2-norms respectively.

Coming soon...

Simulating robust designs

How to generate samples of uncertain parameters...

How to perform a Monte Carlo based probability of failure analysis based on constraint satisfaction, i.e. feasibility solves...