Adjustable robust optimization with nonlinear recourses

Abstract

Over the last century, mathematical optimization has become a prominent tool for decision making.Its systematic application in practical fields such as economics, logistics or defence led to the development of algorithmic methods with ever increasing efficiency. Indeed, for a variety of realworld problems, finding an optimal decision among a set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the research community raised more and more attention to the role of uncertainty in the optimization process.In particular, one may question the notion of optimality, and even feasibility, when studying