  Two of the most important areas in computational finance: Greeks and,
respectively, calibration, are based on efficient and accurate computation of a
large number of sensitivities. This paper gives an overview of adjoint and
automatic differentiation (AD), also known as algorithmic differentiation,
techniques to calculate these sensitivities. When compared to finite difference
approximation, this approach can potentially reduce the computational cost by
several orders of magnitude, with sensitivities accurate up to machine
precision. Examples and a literature survey are also provided.
