On joint probabilistic constraints with Gaussian coefficient matrix

The paper deals with joint probabilistic constraints defined by a Gaussian coefficient matrix. It is shown how to explicitly reduce the computation of values and gradients of the underlying probability function to that of Gaussian distribution functions. This allows us to employ existing efficient algorithms for calculating this latter class of functions in order to solve probabilistically constrained optimization problems of the indicated type. Results are illustrated by an example from energy production.