Automatic differentiation: Obtaining fast and reliable derivatives -- fast

摘要

In this paper, the authors introduce automatic differentiation as a method for computing derivatives of large computer codes. After a brief discussion of methods of differentiating codes, they review automatic differentiation and introduce the ADIFOR (Automatic DIfferentiation of FORtran) tool. They highlight some applications of ADIFOR to large industrial and scientific codes (groundwater transport, CFD airfoil design, and sensitivity-enhanced MM5 mesoscale weather model), and discuss the effectiveness and performance of their approach. Finally, they discuss sparsity in automatic differentiation and introduce the SparsLinC library.