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Data · dataset · 2016

Optimized higher-order automatic differentiation for the Faddeeva function

Listed in Teesside University Research Data Repository

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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.

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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)

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Teesside University Research Data Repositoryoai:data.mendeley.com/76gp8sd2z3.115 h agoJSON v1
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