Advances in stochastic programming approaches to optimization under uncertainty
The habilitation thesis, Advances in Stochastic Programming Approaches to Optimization under Uncertainty, is based on a collection of ten papers authored, and co authored, by Martin Branda. The thesis organizes the discussion into work on: (i) chance constrained optimization models; (ii) sample approximation of mixed integer stochastic programs; and, (iii) extensions of the data envelopment analysis technique to capture diversification, with applications in portfolio optimization.