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Adifor 2.0: automatic differentiation of Fortran 77 programs
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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Citations
Cited by
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Helton, J. C., & Davis, F. J. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(03)00058-9 · Cited by 2,688
JuMP: A Modeling Language for Mathematical Optimization
Dunning, I., Huchette, J., & Lubin, M. (2017) · SIAM Review · doi:10.1137/15m1020575 · Cited by 1,250
Identification and Review of Sensitivity Analysis Methods
Christopher Frey, H., & Patil, S. R. (2002) · Risk Analysis · doi:10.1111/0272-4332.00039 · Cited by 1,036
Metadata
- Authors
- Bischof, C., Khademi, P., Mauer, A., & Carle, A.
- Venue
- IEEE Computational Science and Engineering
- Published
- 1996
- Volume
- 3
- Issue
- 3
- Pages
- 18-32
- ISSN
- 1070-9924 (ISSN-L)
- OpenAlex
- W2146534684
- Cited by
- 366
- References
- 0
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Adifor 2.0: automatic differentiation of Fortran 77 programs. https://app.cochat.ai/papers/10.1109/99.537089
- The paper
- Bischof, C., Khademi, P., Mauer, A., & Carle, A. (1996). Adifor 2.0: automatic differentiation of Fortran 77 programs. IEEE Computational Science and Engineering. https://doi.org/10.1109/99.537089
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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