Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics, Series Number 19)

Algorithmic, or automatic, differentiation (AD) is concerned with the accurate and efficient evaluation of derivatives for functions defined by computer programs. No truncation errors are incurred, and the resulting numerical derivative values can be used for all scientific computations that are based on linear, quadratic, or even higher order approximations to nonlinear scalar or vector functions. In particular, AD has been applied to optimization, parameter identification, equation solving, the numerical integration of differential equations, and combinations thereof. Apart from quantifying sensitivities numerically, AD techniques can also provide structural information, e.g., sparsity pattern and generic rank of Jacobian matrices.

Andreas Griewank is a former Senior Scientist of the Mathematics and Computer Science Division, Argonne National Laboratory. He is currently a Professor at the Institute of Scientific Computing in the Department of Mathematics at the Technical University Dresden.