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Related papers: TaylUR, an arbitrary-order diagonal automatic diff…

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We present a new version of TaylUR, a Fortran 95 module to automatically compute the numerical values of a complex-valued function's derivatives with respect to several variables up to an arbitrary order in each variable, but excluding…

Computational Physics · Physics 2007-05-23 Georg M. von Hippel

This new version of TaylUR is based on a completely new core, which now is able to compute the numerical values of all of a complex-valued function's partial derivatives up to an arbitrary order, including mixed partial derivatives.

Computational Physics · Physics 2010-01-21 G. M. von Hippel

ADF95 is a tool to automatically calculate numerical first derivatives for any mathematical expression as a function of user defined independent variables. Accuracy of derivatives is achieved within machine precision. ADF95 may be applied…

Mathematical Software · Computer Science 2007-05-23 Christian W. Straka

Dual numbers are a well-established tool for computing derivatives and constitute the basis of forward-mode automatic differentiation. While the theoretical framework for computing derivatives of arbitrary order is well understood,…

Numerical Analysis · Mathematics 2026-02-06 F. Peñuñuri , K. B. Cantún-Avila , R. Peón-Escalante

Most numerical solvers used to determine free variables of dynamical systems rely on first-order derivatives of the state of the system w.r.t. the free variables. The number of the free variables can be fairly large. One of the approaches…

Mathematical Software · Computer Science 2019-05-27 Ivan Dolgakov , Dmitry Pavlov

Taylor series methods show a newfound promise for the solution of non-stiff ordinary differential equations (ODEs) given the rise of new compiler-enhanced techniques for calculating high order derivatives. In this paper we detail a new…

Numerical Analysis · Mathematics 2026-02-20 Songchen Tan , Oscar Smith , Christopher Rackauckas

Higher order derivatives of functions are structured high dimensional objects which lend themselves to many alternative representations, with the most popular being multi-index, matrix and tensor representations. The choice between them…

Classical Analysis and ODEs · Mathematics 2021-12-01 José E. Chacón , Tarn Duong

High-order derivatives of nested functions of a single variable can be computed with the celebrated Fa\`a di Bruno's formula. Although generalizations of such formula to multiple variables exist, their combinatorial nature generates an…

Numerical Analysis · Mathematics 2019-11-27 Filippo M. Miatto

This paper provides a new approach to derive various arbitrary high order finite difference formulae for the numerical differentiation of analytic functions. In this approach, various first and second order formulae for the numerical…

Numerical Analysis · Mathematics 2020-05-26 Saint-Cyr E. R. Koyaguerebo-Imé , Yves Bourgault

We develop a compositional approach for automatic and symbolic differentiation based on categorical constructions in functional analysis where derivatives are linear functions on abstract vectors rather than being limited to scalars,…

Programming Languages · Computer Science 2022-07-05 Martin Elsman , Fritz Henglein , Robin Kaarsgaard , Mikkel Kragh Mathiesen , Robert Schenck

aITALC, a new tool for automating loop calculations in high energy physics, is described. The package creates Fortran code for two-fermion scattering processes automatically, starting from the generation and analysis of the Feynman graphs.…

High Energy Physics - Phenomenology · Physics 2009-11-10 Alejandro Lorca , Tord Riemann

High-order Lie derivatives are essential in nonlinear systems analysis. If done symbolically, their evaluation becomes increasingly expensive as the order increases. We present a compact and efficient numerical approach for computing Lie…

Numerical Analysis · Mathematics 2026-01-19 Nedialko S. Nedialkov , John D. Pryce

Recent theoretical work on automatic differentiation (autodiff) has focused on characteristics such as correctness and efficiency while assuming that all derivatives are automatically generated by autodiff using program transformation, with…

Programming Languages · Computer Science 2024-08-15 Sam Estep

The derivatives of a Boolean function are defined up to any order. The Taylor and MacLaurin expansions of a Boolean function are thus obtained. The last corresponds to the ring sum expansion (RSE) of a Boolean function, and is a more…

Condensed Matter · Physics 2007-05-23 Franco Bagnoli

A variety of problems emerged investigating electronic circuits, computer devices and cellular automata motivated a number of attempts to create a differential and integral calculus for Boolean functions. In the present article, we extend…

Logic · Mathematics 2016-08-17 Eduardo Mizraji

We construct a complex entire function with arbitrary number of variables which has the following property: The infinite set consisting of all the values of all its partial derivatives of any orders at all algebraic points, including zero…

Number Theory · Mathematics 2022-08-04 Haruki Ide , Taka-aki Tanaka

The foundational theory of differentiation was developed as part of the original release of ACL2(r). In work reported at the last ACL2 Workshop, we presented theorems justifying the usual differentiation rules, including the chain rule and…

Symbolic Computation · Computer Science 2011-10-24 Peter Reid , Ruben Gamboa

Modern astronomical potentials modeling galaxies or stellar systems can be rather involved, and deriving their first derivatives (accelerations) and second derivatives (variational equations) in order to compute orbits and their chaoticity…

Astrophysics of Galaxies · Physics 2022-06-01 Daniel D. Carpintero , Nicolás P. Maffione , Facundo A. Gómez

In the present article, a new method for the evaluation of fractional derivatives of arbitrary real order is proposed. Numerous but inequivalent formulations have been given in the past. Some of them exhibit unsatisfactory properties such…

Functional Analysis · Mathematics 2021-05-04 Cyril Belardinelli

Taylor's formula holds significant importance in function representation, such as solving differential difference equations, ordinary differential equations, partial differential equations, and further promotes applications in visual…

Machine Learning · Computer Science 2025-07-15 Guoyou Wang , Yihua Tan , Shiqi Liu
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