Probabilistic programming means two strongly connected models as well as the study of
their mathematical properties, solutions of the relevant optimization problems and their …

The behaviour of stochastic programming problems is studied in case of the underlying
probability distribution being perturbed and approximated, respectively. Most of the …

This is a substantial revision of the previous edition with added new material. The
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …

The advent of high-speed computers and sophisticated software tools has made the
computation of derivatives for functions defined by evaluation programs both easier and …

Variational analysis has been recognized as a fruitful area in mathematics that on the one
hand deals with the study of optimization and equilibrium problems and on the other hand …

In this paper, we study the link between a Chance-Constrained optimization Problem (CCP)
and its sample counterpart (SP). SP has a finite number, say N, of sampled constraints …

Linear programs with joint probabilistic constraints (PCLP) are difficult to solve because the
feasible region is not convex. We consider a special case of PCLP in which only the right …

Abstract The Unit Commitment problem in energy management aims at finding the optimal
production schedule of a set of generation units, while meeting various system-wide …

The paper is devoted to the analysis of the calmness property for constraint set mappings.
After some general characterizations, specific results are obtained for various types of …

Quantitative stability of optimal values and solution sets to stochastic programming problems
is studied when the underlying probability distribution varies in some metric space of …