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On the Automatic Differentiation of Computer Programs and an Application to Multibody Systems

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Part of the book series: Solid Mechanics and its Applications ((SMIA,volume 43))

Abstract

Automatic differentiation (AD) is a methodology for developing sensitivity-enhanced versions of arbitrary computer programs. In this paper, we provide some background information on AD and address some frequently asked questions. We introduce the ADIFOR and ADIC tools for the automatic differentiation of Fortran 77 and ANSI-C programs, respectively, and give an example of applying ADIFOR in the context of the optimization of multibody systems.

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References

  1. Dieter Bestle. Analyse und Optimierung von Mehrkörpersystemen. Springer, Berlin, 1994.

    Google Scholar 

  2. Dieter Bestle and Peter Eberhard. Analyzing and optimizing multibody systems. Mechanical Structures and Machinery, 20(1):67–92, 1992.

    Article  Google Scholar 

  3. Christian Bischof, Alan Carle, George Corliss, Andreas Griewank, and Paul Hovland. ADIFOR: Generating derivative codes from Fortran programs. Scientific Programming, 1(1):11–29, 1992.

    Google Scholar 

  4. Christian Bischof, Alan Carle, Peyvand Khademi, and Andrew Mauer. The ADIFOR 2.0 system for the automatic differentiation of Fortran 77 programs, 1994. Preprint MCS-P481-1194, Mathematics and Computer Science Division, Argonne National Laboratory, and CRPC-TR94491, Center for Research on Parallel Computation, Rice University.

    Google Scholar 

  5. Christian Bischof and Andrew Mauer. ADIC – A tool for the automatic differentiation of C programs. Preprint MCS-P499-0295, Mathematics and Computer Science Division, Argonne National Laboratory, 1995.

    Google Scholar 

  6. Christian Bischof, Greg Whiffen, Christine Shoemaker, Alan Carle, and Aaron Ross. Application of automatic differentiation to groundwater transport models. In Alexander Peters et al., editors, Computational Methods in Water Resources X, pages 173–182. Kluwer Academic Publishers, Dordrehct, 1994.

    Google Scholar 

  7. Francois Bodin, Peter Beckman, Dennis Gannon, Jacob Goutwals, Srinivas Narayana, Suresh Srinivas, and Beata Winnicka. SAGE++: An object-oriented toolkit and class library for building Fortran and C++ restructuring tools. In Proceedings of the Second Annual Object-Oriented Numerics Conference. IEEE, 1994.

    Google Scholar 

  8. D. Callahan, K. Cooper, R. T. Hood, K. Kennedy, and L. M. Torczon. ParaScope: A parallel programming environment. International Journal of Supercomputer Applications, 2(4):84–99, December 1988.

    Article  Google Scholar 

  9. Herbert Fischer. Special problems in automatic differentiation. In Andreas Griewank and George F. Corliss, editors, Automatic Differentiation of Algorithms: Theory, Implementation, and Application, pages 43 – 50. SIAM, Philadelphia, Penn., 1991.

    Google Scholar 

  10. Andreas Griewank. On automatic differentiation. In Mathematical Programming: Recent Developments and Applications, pages 83–108. Kluwer Academic Publishers, Amsterdam, 1989.

    Google Scholar 

  11. Andreas Griewank, Christian Bischof, George Corliss, Alan Carle, and Karen Williamson. Derivative convergence of iterative equation solvers. Optimization Methods and Software, 2:321–355, 1993.

    Article  Google Scholar 

  12. Andreas Griewank and Shawn Reese. On the calculation of Jacobian matrices by the Markowitz rule. In Andreas Griewank and George F. Corliss, editors, Automatic Differentiation of Algorithms: Theory, Implementation, and Application, pages 126–135. SIAM, Philadelphia, 1991.

    Google Scholar 

  13. Uli Häußermann. Automatische Differentiation zur Rekursiven Bestimmung von Partiellen Ableitungen. STUD-102, Institut B für Mechanik, Universität Stuttgart, 1993.

    Google Scholar 

  14. E. Kreuzer and G. Leister. Programmsystem NEWEUL’90. Technical Report Anleitung AN-24, Institut B für Mechanik, Universität Stuttgart, 1991.

    Google Scholar 

  15. G. Leister and W. Schiehlen. Benchmark-beispiele des DFG-schwerpunktprogramms dynamic von mehrkörpersystemen. Technical Report Zwischenbericht ZB-64, Band 2, Institut B für Mechanik, Universität Stuttgart, 1991.

    Google Scholar 

  16. Louis B. Rail. Automatic Differentiation: Techniques and Applications, volume 120 of Lecture Notes in Computer Science. Springer Verlag, Berlin, 1981

    Google Scholar 

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© 1996 Kluwer Academic Publishers

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Bischof, C.H. (1996). On the Automatic Differentiation of Computer Programs and an Application to Multibody Systems. In: Bestle, D., Schiehlen, W. (eds) IUTAM Symposium on Optimization of Mechanical Systems. Solid Mechanics and its Applications, vol 43. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0153-7_6

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  • DOI: https://doi.org/10.1007/978-94-009-0153-7_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6555-9

  • Online ISBN: 978-94-009-0153-7

  • eBook Packages: Springer Book Archive

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