14h00 - 15h00
Derivative-free robust optimization by outer approximations
We present an algorithm for minimax problems that arise in robust optimization in the absence of objective function derivatives. This important class of problems includes design under uncertainty, for cases where some design evaluations are only available through experiment or simulation. The algorithm utilizes an extension of methods for inexact outer approximation in sampling a potentially infinite-cardinality uncertainty set. Clarke stationarity of the algorithm output is established alongside desirable features of the model-based trust-region subproblems encountered. We demonstrate the practical benefits of the algorithm on a new class of test problems.