Many optimization problems can be modelled as nonlinear programming problems (NLP). Optimization problems arise from a large number of different applications, such as chemical and electrical engineering and economics. For example, in chemical engineering the aim is to maximize the efficiency of a chemical plant under certain operating restrictions. The model of the efficiency forms the objective function and the operating restrictions form the constraints. In electrical engineering, fuel costs or system losses are minimized in electrical networks. The fuel cost can be expressed as a function of the generated power. This function is used as the objective function. The constraints are given by the consumer demand for power, which has to be met and by certain system restrictions. In order to provide an accurate enough model for the plant or network, it is often necessary to use nonlinear functions for both objective function and constraint functions. Higher accuracy of the model also leads to a larger number of variables and constraints. Some models even require non-smooth constraint functions. If this is the case, N LP methods cannot solve the problem, because first and second derivatives do not exist for all points of the constraint functions.