ADIFOR - A FORTRAN system for portable automatic differentiation
Christian H Bischof, Andreas Griewank
Abstract
Christian H Bischof, Andreas Griewank
Abstract
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design optimization of complex systems by reliably computing derivatives of large computer codes, with the potential of doing it many times faster compared to current approaches. This paper describes the ADIFOR (Automatic DIfferentiation of FORtran) system, a translator that augments Fortran programs with statements for the computation of derivatives. ADIFOR accepts arbitrary Fortran 77 code defining the computation of a function and writes portable Fortran 77 code for the computation of its derivatives. Our goal is to free the computational scientist from worrying about the accurate and efficient computation of derivatives, even for complicated "functions", thereby enabling him to concentrate on the more important issues of system modeling and algorithm design. This paper gives an overview of the principles underlying the ADIFOR system, and comments on the power of automatic differentiation for c...
OpenAlex reports 37 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design optimization of complex systems by reliably computing derivatives of large computer codes, with the potential of doing it many times faster compared to current approaches. This paper describes the ADIFOR (Automatic DIfferentiation of FORtran) system, a translator that augments Fortran programs with statements for the computation of derivatives. ADIFOR accepts arbitrary Fortran 77 code defining the computation of a function and writes portable Fortran 77 code for the computation of its derivatives. Our goal is to free the computational scientist from worrying about the accurate and efficient computation of derivatives, even for complicated "functions", thereby enabling him to concentrate on the more important issues of system modeling and algorithm design. This paper gives an overview of the principles underlying the ADIFOR system, and comments on the power of automatic differentiation for c...
Key concepts: Fortran, Automatic differentiation, Computer science, Computation, Programming language, Code (set theory), Function (biology), Computational science