Abstract: This paper gives an overview of methods for computing derivative information in dynamic optimization with path constraints. Efficiency of forward and adjoint techniques are discussed in a discrete-time setting and some algorithms are derived. Next, the discussion is extended to also include continuous-discrete systems. Dimensions in the model, signal parameterization, horizon length and sampling interval affect each of the methods differently. The key contributions of this paper is to give an overview of these methods, how they can be combined, and how different parameters affect efficiency
