Efficient derivative codes through automatic differentiation and interface contraction: An application in biostatistics

Developing code for computing the first- and higher-order derivatives of a function by hand can be very time-consuming and is prone to errors. Automatic differentiation has proven capable of producing derivative codes with very little effort on the part of the user. Automatic differentiation avoids the truncation errors characteristic of divided-difference approximations. However, the derivative code produced by automatic differentiation can be significantly less efficient than one produced by hand. This shortcoming may be overcome by utilizing insight into the high-level structure of a computation. This paper focuses on how to take advantage of the fact that the number of variables passed between subroutines frequently is small compared with the number of the variables with respect to which they wish to differentiate. Such an interface contraction, coupled with the associativity of the chain rule for differentiation, allows them to apply automatic differentiation in a more judicious fashion, resulting in much more efficient code for the computation of derivatives. A case study involving a program for maximizing a logistic-normal likelihood function developed from a problem in nutritional epidemiology is examined, and performance figures are presented. This paper concludes with some directions for future study