![]() ![]() ![]() TAF accepts Fortran 77-95 code as input, applies a semantic transformation, and generates Fortran 77-95 code. Transformation of Algorithms in Fortran (TAF): source-to-source translator for Fortran 77-95 code, i.e. It can be run online or be downloaded and installed locally as a set of Java classes (JAR archive). Tapenade: automatic differentiation engine developed at Inria. OpenAD: tool for automatic differentiation (AD) of numerical computer programs The benefit of this approach is that interfaces and implementations of mathematical functions can be "injected" into the modules where they are used.įazang: library for reverse-mode automatic differentiation, inspired by Stan/Math library, by Yi Zhang ![]() and Blair, M.: "DNAD, a Simple Tool for Automatic Differentiation of Fortran Codes Using Dual Numbers," Computer Physics Communications, vol. The mathematical function should be expressed as one or more fortran 77/90/95 procedures.Īutodiff: automatic differentiation for up to 4th derivatives, by Simon GeardĪuto-Diff: implementation in Modern Fortran of backward mode automatic differentiation, by zoziha.ĭual Number Automatic Differentiation (DNAD): update of code from Yu, W. Farantos, part of the Computer Physics Communications Program Library. The mathematical operators have been overloaded to work with the newly defined types, which include not only the function value, but also the gradient, Hessian and Laplacian.Īuto_deriv: module comprised of a set of Fortran 95 procedures which can be used to calculate the first and second partial derivatives (mixed or not) of any continuous function with many independent variables, by S. ![]() Straka, Computer Physics Communications, Volume 168, Issue 2, 1 June 2005, Pages 123-139, with preprint hereĪdifor: given a Fortran 77 source code and a user's specification of dependent and independent variables, ADIFOR will generate an augmented derivative code that computes the partial derivatives of all of the specified dependent variables with respect to all of the specified independent variables in addition to the original result.Īdjac: automatic differentiation for generating sparse Jacobians, using Fortran 95 and operator overloading, by Pauli VirtanenĪudito: automatic differentiation tool for Fortran, by Michel V. It has no limit in terms of the number of independent variables (this number is defined at runtime) and can compute up to third derivatives.ĪDF95: modification of Jingchang Shi to work with gfortran of program described in ADF95: Tool for automatic differentiation of a FORTRAN code designed for large numbers of independent variables, by Christian W.
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