Optimized higher-order automatic differentiation for the Faddeeva function

Published: 29 Nov 2019 | Version 1 | DOI: 10.17632/76gp8sd2z3.1
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Description of this data

Abstract
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:

Optimized higher-order automatic differentiation for the Faddeeva function

Published in: Computer Physics Communications

Latest version

  • Version 1

    2019-11-29

    Published: 2019-11-29

    DOI: 10.17632/76gp8sd2z3.1

    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

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Categories

Computational Physics, Computational Method

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