In this paper we use Lagrange interpolation polynomials to obtain good gradient estimations. This is e.g. important for nonlinear programming solvers. As an error criterion we take the mean squared error. This error can be split up into a deterministic and a stochastic error. We analyze these...

In this paper we analyze different schemes for obtaining gradient estimates when the underlying function is noisy. Good gradient estimation is e.g. important for nonlinear programming solvers. As an error criterion we take the norm of the difference between the real and estimated gradients. This...

This paper determines the optimal timing of dike heightenings as well as the corresponding optimal dike heightenings to protect against floods. To derive the optimal policy we design an algorithm based on the Impulse Control Maximum Principle. In this way the paper presents one of the first real...

Robust optimization is a methodology that can be applied to problems that are affected by uncertainty in the problem's parameters. The classical robust counterpart (RC) of the problem requires the solution to be feasible for all uncertain parameter values in a so-called uncertainty set, and...

In this paper, we introduce a framework designed to identify and rank possible unwarranted variation of treatments in healthcare. The innovative aspect of this framework is a ranking procedure that aims to identify healthcare institutions where unwarranted variation is most severe, and diagnosis...

Much research focuses on development of new agricultural technologies to reduce poverty levels of the large population of smallholder farms in Sub Saharan Africa. In this paper we argue that smallholders can also increase their production in a different way, namely by using their resources more...