Recent Advances in Algorithmic Differentiation

1. An Integer Programming Approach to Optimal Derivative Accumulation

   • Jieqiu Chen, Paul Hovland, Todd Munson, Jean Utke

   Pages 221-231

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2. The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set

   • Torsten Bosse, Andreas Griewank

   Pages 233-240

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3. Java Automatic Differentiation Tool Using Virtual Operator Overloading

   • Phuong Pham-Quang, Benoit Delinchant

   Pages 241-250

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4. High-Order Uncertainty Propagation Enabled by Computational Differentiation

   • Ahmad Bani Younes, James Turner, Manoranjan Majji, John Junkins

   Pages 251-260

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5. Generative Programming for Automatic Differentiation

   • Marco Nehmeier

   Pages 261-271

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6. AD in Fortran: Implementation via Prepreprocessor

   • Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind

   Pages 273-284

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7. An AD-Enabled Optimization ToolBox in LabVIEWTM

   • Abhishek Kr. Gupta, Shaun A. Forth

   Pages 285-295

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8. CasADi: A Symbolic Package for Automatic Differentiation and Optimal Control

   • Joel Andersson, Johan Åkesson, Moritz Diehl

   Pages 297-307

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9. Efficient Expression Templates for Operator Overloading-Based Automatic Differentiation

   • Eric Phipps, Roger Pawlowski

   Pages 309-319

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10. Computing Derivatives in a Meshless Simulation Using Permutations in ADOL-C

    • Kshitij Kulshreshtha, Jan Marburger

    Pages 321-331

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11. Lazy K-Way Linear Combination Kernels for Efficient Runtime Sparse Jacobian Matrix Evaluations in C++

    • Rami M. Younis, Hamdi A. Tchelepi

    Pages 333-342

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12. Implementation of Partial Separability in a Source-to-Source Transformation AD Tool

    • Sri Hari Krishna Narayanan, Boyana Norris, Paul Hovland, Assefaw Gebremedhin

    Pages 343-353

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13. Back Matter

    Pages 355-361

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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.

• adjoint computation
• algorithmic differentiation
• optimization
• sensitivity analysis
• uncertainty quantification

• 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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