Arbogast: Higher order automatic differentiation for special functions with Modular C

DOI10.1080/10556788.2018.1428603zbMath1455.65006OpenAlexW2793269400WikidataQ105651611 ScholiaQ105651611MaRDI QIDQ4685591

Jens Gustedt, Isabelle Charpentier

Publication date: 9 October 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2018.1428603

zbMATH Keywords

automatic differentiationdifferential operatorsmodular programmingCfunctions of mathematical physicscontextualization

Mathematics Subject Classification ID

Symbolic computation and algebraic computation (68W30) Numerical differentiation (65D25) Software, source code, etc. for problems pertaining to numerical analysis (65-04)

Related Items (3)

Towards a full higher order AD-based continuation and bifurcation framework ⋮ Analytical and asymptotic evaluations of Dawson's integral and related functions in mathematical physics ⋮ Arbogast

Uses Software

• ADIC
• EISPACK
• DLMF
• Cosy
• Rapsodia
• HOAD_MathFun

Cites Work

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• Higher-order automatic differentiation of mathematical functions
• A generic approach for the solution of nonlinear residual equations. II: Homotopy and complex nonlinear eigenvalue method
• Optimized higher-order automatic differentiation for the Faddeeva function
• On higher-order differentiation in nonlinear mechanics
• Evaluating Derivatives
• Fast higher-order derivative tensors with Rapsodia
• The Curious History of Faa di Bruno's Formula
• Towards a full higher order AD-based continuation and bifurcation framework
• Evaluating higher derivative tensors by forward propagation of univariate Taylor series
• Eigenvalue Problems in Fiber Optic Design
• Prehistory of Faà   di Bruno's Formula

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