University of North Carolina at Chapel Hill
The efficient application of automatic differentiation for computing gradients in financial applications
Automatic differentiation is the theme of this paper. The authors show that many functions in calibration and inverse problems, exhibit a natural substitution structure. A significant speedup is achieved compared with common reverse-mode AD.