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1996•IEEE Computational Science and EngineeringRequires access

Adifor 2.0: automatic differentiation of Fortran 77 programs

Christian H Bischof, P. Khademi, Andrew Mauer, Alan Carle

Open publisher page 370 citations

Abstract

Numerical codes that calculate not only a result, but also the derivatives of the variables with respect to each other, facilitate sensitivity analysis, inverse problem solving, and optimization. The paper considers how Adifor 2.0, which won the 1995 Wilkinson Prize for Numerical Software, can automatically differentiate complicated Fortran code much faster than a programmer can do it by hand. The Adifor system has three main components: the AdiFor preprocessor, the ADIntrinsics exception-handling system, and the SparsLinC library.

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What this paper is about

Numerical codes that calculate not only a result, but also the derivatives of the variables with respect to each other, facilitate sensitivity analysis, inverse problem solving, and optimization. The paper considers how Adifor 2.0, which won the 1995 Wilkinson Prize for Numerical Software, can automatically differentiate complicated Fortran code much faster than a programmer can do it by hand. The Adifor system has three main components: the AdiFor preprocessor, the ADIntrinsics exception-handling system, and the SparsLinC library.

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OpenAlex reports 370 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Numerical codes that calculate not only a result, but also the derivatives of the variables with respect to each other, facilitate sensitivity analysis, inverse problem solving, and optimization. The paper considers how Adifor 2.0, which won the 1995 Wilkinson Prize for Numerical Software, can automatically differentiate complicated Fortran code much faster than a programmer can do it by hand. The Adifor system has three main components: the AdiFor preprocessor, the ADIntrinsics exception-handling system, and the SparsLinC library.

Key concepts: Fortran, Programmer, Preprocessor, Automatic differentiation, Computer science, Programming language, Code (set theory), Software

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