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Recent Advances in Algorithmic Differentiation

  • Conference proceedings
  • © 2012

Overview

Editors:
  1. Shaun Forth

    1. Shrivenham Campus, Applied Mathematics & Scientific Computi, Cranfield University, Swindon, United Kingdom

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  2. Paul Hovland

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

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  3. Eric Phipps

    1. Sandia National Laboratory, Albuquerque, USA

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  4. Jean Utke

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

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  5. Andrea Walther

    1. , Mathematics, University of Paderborn, Paderborn, Germany

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  • Easily accessible explanations that do not require a priori in-depth expertise Covers topics for users, researchers, and tool developers in the algorithmic differentiation area
  • This collection is the most comprehensive and recent source of information on the subject since the AD2008 proceedings
  • Includes supplementary material: sn.pub/extras

Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 87)

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Table of contents (31 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. A Leibniz Notation for Automatic Differentiation

    • Bruce Christianson
    Pages 1-9

  3. Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics

    • Daniel R. Reynolds, Ravi Samtaney
    Pages 11-21

  4. Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models

    • James A. Reed, Jean Utke, Hany S. Abdel-Khalik
    Pages 23-33

  5. Application of Automatic Differentiation to an Incompressible URANS Solver

    • Emre Özkaya, Anil Nemili, Nicolas R. Gauger
    Pages 35-45

  6. Applying Automatic Differentiation to the Community Land Model

    • Azamat Mametjanov, Boyana Norris, Xiaoyan Zeng, Beth Drewniak, Jean Utke, Mihai Anitescu et al.
    Pages 47-57

  7. Using Automatic Differentiation to Study the Sensitivity of a Crop Model

    • Claire Lauvernet, Laurent Hascoët, François-Xavier Le Dimet, Frédéric Baret
    Pages 59-69

  8. Efficient Automatic Differentiation of Matrix Functions

    • Peder A. Olsen, Steven J. Rennie, Vaibhava Goel
    Pages 71-81

  9. Native Handling of Message-Passing Communication in Data-Flow Analysis

    • Valérie Pascual, Laurent Hascoët
    Pages 83-92

  10. Increasing Memory Locality by Executing Several Model Instances Simultaneously

    • Ralf Giering, Michael Voßbeck
    Pages 93-101

  11. Adjoint Mode Computation of Subgradients for McCormick Relaxations

    • Markus Beckers, Viktor Mosenkis, Uwe Naumann
    Pages 103-113

  12. Evaluating an Element of the Clarke Generalized Jacobian of a Piecewise Differentiable Function

    • Kamil A. Khan, Paul I. Barton
    Pages 115-125

  13. The Impact of Dynamic Data Reshaping on Adjoint Code Generation for Weakly-Typed Languages Such as Matlab

    • Johannes Willkomm, Christian H. Bischof, H. Martin Bücker
    Pages 127-138

  14. On the Efficient Computation of Sparsity Patterns for Hessians

    • Andrea Walther
    Pages 139-149

  15. Exploiting Sparsity in Automatic Differentiation on Multicore Architectures

    • Benjamin Letschert, Kshitij Kulshreshtha, Andrea Walther, Duc Nguyen, Assefaw Gebremedhin, Alex Pothen
    Pages 151-161

  16. Automatic Differentiation Through the Use of Hyper-Dual Numbers for Second Derivatives

    • Jeffrey A. Fike, Juan J. Alonso
    Pages 163-173

  17. Connections Between Power Series Methods and Automatic Differentiation

    • David C. Carothers, Stephen K. Lucas, G. Edgar Parker, Joseph D. Rudmin, James S. Sochacki, Roger J. Thelwell et al.
    Pages 175-185

  18. Hierarchical Algorithmic Differentiation A Case Study

    • Johannes Lotz, Uwe Naumann, Jörn Ungermann
    Pages 187-196

  19. Storing Versus Recomputation on Multiple DAGs

    • Heather Cole-Mullen, Andrew Lyons, Jean Utke
    Pages 197-207

  20. Using Directed Edge Separators to Increase Efficiency in the Determination of Jacobian Matrices via Automatic Differentiation

    • Thomas F. Coleman, Xin Xiong, Wei Xu
    Pages 209-219
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Keywords

About this book

The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD). The 31 contributed papers presented at the AD2012 conference cover the application of AD to many areas in science and engineering as well as aspects of AD theory and its implementation in tools. For all papers the referees, selected from the program committee and the greater community, as well as the editors have emphasized accessibility of the presented ideas also to non-AD experts. In the AD tools arena new implementations are introduced covering, for example, Java and graphical modeling environments or join the set of existing tools for Fortran. New developments in AD algorithms target the efficiency of matrix-operation derivatives, detection and exploitation of sparsity, partial separability, the treatment of nonsmooth functions, and other high-level mathematical aspects of the numerical computations to be differentiated. Applications stem from the Earth sciences, nuclear engineering, fluid dynamics, and chemistry, to name just a few. In many cases the applications in a given area of science or engineering share characteristics that require specific approaches to enable AD capabilities or provide an opportunity for efficiency gains in the derivative computation. The description of these characteristics and of the techniques for successfully using AD should make the proceedings a valuable source of information for users of AD tools.

Editors and Affiliations

  • Shrivenham Campus, Applied Mathematics & Scientific Computi, Cranfield University, Swindon, United Kingdom

    Shaun Forth

  • Mathematics and Computer Science Div., Argonne National Laboratory, Argonne, USA

    Paul Hovland, Jean Utke

  • Sandia National Laboratory, Albuquerque, USA

    Eric Phipps

  • , Mathematics, University of Paderborn, Paderborn, Germany

    Andrea Walther

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