Stochastic and Robust Approaches to Optimization Problems under Uncertainty

In the last decade, optimization models under uncertainty have drawn much attention and efficient algorithms have been developed for solving those problems. This article presents the author´s recent attempts conducted in collaboration with a number of co-authors to deal with uncertainty in various optimization problems including complementarity problems, mathematical programs with equilibrium constraints, as well as applications in data mining, mathematical finance, and game theory