Differentiating Fixed Point Iterations with ADOL-C: Gradient Calculation for Fluid Dynamics

The reverse mode of automatic differentiation allows the computation of gradients at a temporal complexity that is only a small multiple of the temporal complexity to evaluate the function itself. However, the memory requirement of the reverse mode in its basic form is proportional to the operation count of the function to be differentiated. For iterative processes consisting of iterations with uniform complexity this means that the memory requirement of the reverse mode grows linearly with the number of iterations. For fixed point iterations this is not efficient, since any structure of the problem is neglected.