Optimized higher-order automatic differentiation for the Faddeeva function
Description of this data
Considerable research efforts have been directed at implementing the Faddeeva function w ( z ) and its derivatives with respect to z , but these did not consider the key computing issue of a possible dependence of z on some variable t . The general case is to differentiate the compound function w ( z ( t ) ) = w ∘ z ( t ) with respect to t by applying the chain rule for a first order derivative, or Faà di Bruno’s formula for higher-order ones. Higher-order automatic differentiation (HOAD) is ...
Title of program: HOAD_MathFun
Catalogue Id: AFAG_v1_0
Nature of problem
General optimized higher-order automatic differentiation of mathematical functions. Complex refractive index as a case study.
Versions of this program held in the CPC repository in Mendeley Data
AFAG_v1_0; HOAD_MathFun; 10.1016/j.cpc.2016.04.009
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
Experiment data files
This data is associated with the following publication:
Cite this dataset
Charpentier, Isabelle (2019), “Optimized higher-order automatic differentiation for the Faddeeva function ”, Mendeley Data, v1 http://dx.doi.org/10.17632/76gp8sd2z3.1
The files associated with this dataset are licensed under a CPC_SPECIAL licence.