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Automatic Differentiation: Applications, Theory, and Implementations

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Martin Bücker

    1. Institute for Scientific Computing, RWTH Aachen University, Aachen, Germany

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  2. George Corliss

    1. Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

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  3. Uwe Naumann

    1. Software and Tools for Computational Engineering, RWTH Aachen University, Aachen, Germany

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  4. Paul Hovland

    1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

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  5. Boyana Norris

    1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

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Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 50)

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Table of contents (28 papers)

  1. Front Matter

    Pages I-XVII
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  2. Perspectives on Automatic Differentiation: Past, Present, and Future?

    • Louis B. Rall
    Pages 1-14

  3. Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities

    • Paul J. Werbos
    Pages 15-34

  4. Solutions of ODEs with Removable Singularities

    • Harley Flanders
    Pages 35-45

  5. Automatic Propagation of Uncertainties

    • Bruce Christianson, Maurice Cox
    Pages 47-58

  6. High-Order Representation of Poincarée Maps

    • Johannes Grote, Martin Berz, Kyoko Makino
    Pages 59-66

  7. Computation of Matrix Permanent with Automatic Differentiation

    • Koichi Kubota
    Pages 67-76

  8. Computing Sparse Jacobian Matrices Optimally

    • Shahadat Hossain, Trond Steihaug
    Pages 77-87

  9. Application of AD-based Quasi-Newton Methods to Stiff ODEs

    • Sebastian Schlenkrich, Andrea Walther, Andreas Griewank
    Pages 89-98

  10. Reduction of Storage Requirement by Checkpointing for Time-Dependent Optimal Control Problems in ODEs

    • Julia Sternberg, Andreas Griewank
    Pages 99-110

  11. Improving the Performance of the Vertex Elimination Algorithm for Derivative Calculation

    • M. Tadjouddine, F. Bodman, J. D. Pryce, S. A. Forth
    Pages 111-120

  12. Flattening Basic Blocks

    • Jean Utke
    Pages 121-133

  13. The Adjoint Data-Flow Analyses: Formalization, Properties, and Applications

    • Laurent Hascoëet, Mauricio Araya-Polo
    Pages 135-146

  14. Semiautomatic Differentiation for Efficient Gradient Computations

    • David M. Gay
    Pages 147-158

  15. Computing Adjoints with the NAGWare Fortran 95 Compiler

    • Uwe Naumann, Jan Riehme
    Pages 159-169

  16. Extension of TAPENADE toward Fortran 95

    • Valéerie Pascual, Laurent Hascoëet
    Pages 171-179

  17. A Macro Language for Derivative Definition in ADiMat

    • Christian H. Bischof, H. Martin Bücker, Andre Vehreschild
    Pages 181-188

  18. Transforming Equation-Based Models in Process Engineering

    • Christian H. Bischof, H. Martin Bücker, Wolfgang Marquardt, Monika Petera, Jutta Wyes
    Pages 189-198

  19. Simulation and Optimization of the Tevatron Accelerator

    • Pavel Snopok, Carol Johnstone, Martin Berz
    Pages 199-209

  20. Periodic Orbits of Hybrid Systems and Parameter Estimation via AD

    • Eric Phipps, Richard Casey, John Guckenheimer
    Pages 211-223
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Keywords

Editors and Affiliations

  • Institute for Scientific Computing, RWTH Aachen University, Aachen, Germany

    Martin Bücker

  • Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

    George Corliss

  • Software and Tools for Computational Engineering, RWTH Aachen University, Aachen, Germany

    Uwe Naumann

  • Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

    Paul Hovland, Boyana Norris

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