Lecture Notes in Computational Science and Engineering

By: Christian H. Bischof (Editor), H. Martin BÃ¼cker (Editor), Paul Hovland (Editor)

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Paperback | 21 July 2008

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The Fifth International Conference on Automatic Differentiation held from August 11 to 15, 2008 in Bonn, Germany, is the most recent one in a series that began in Breckenridge, USA, in 1991 and continued in Santa Fe, USA, in 1996, Nice, France, in 2000 and Chicago, USA, in 2004. The 31 papers included in these proceedings re?ect the state of the art in automatic differentiation (AD) with respect to theory, applications, and tool development. Overall, 53 authors from institutions in 9 countries contributed, demonstrating the worldwide acceptance of AD technology in computational science. Recently it was shown that the problem underlying AD is indeed NP-hard, f- mally proving the inherently challenging nature of this technology. So, most likely, no deterministic "silver bullet" polynomial algorithm can be devised that delivers optimum performance for general codes. In this context, the exploitation of doma- speci?c structural information is a driving issue in advancing practical AD tool and algorithm development. This trend is prominently re?ected in many of the pub- cations in this volume, not only in a better understanding of the interplay of AD and certain mathematical paradigms, but in particular in the use of hierarchical AD approaches that judiciously employ general AD techniques in application-speci?c - gorithmic harnesses. In this context, the understanding of structures such as sparsity of derivatives, or generalizations of this concept like scarcity, plays a critical role, in particular for higher derivative computations.

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Reverse Automatic Differentiation of Linear Multistep Methods	p. 1
Call Tree Reversal is NP-Complete	p. 13
On Formal Certification of AD Transformations	p. 23
Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation	p. 35
A Modification of Weeks' Method for Numerical Inversion of the Laplace Transform in the Real Case Based on Automatic Differentiation	p. 45
A Low Rank Approach to Automatic Differentiation	p. 55
Algorithmic Differentiation of Implicit Functions and Optimal Values	p. 67
Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient	p. 79
A Polynomial-Time Algorithm for Detecting Directed Axial Symmetry in Hessian Computational Graphs	p. 91
On the Practical Exploitation of Scarsity	p. 103
Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation	p. 115
Efficient Higher-Order Derivatives of the Hypergeometric Function	p. 127
The Diainant Approach for an Efficient Automatic Differentiation of the Asymptotic Numerical Method	p. 139
Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals	p. 151
Parallel Reverse Mode Automatic Differentiation for OpenMP Programs with ADOL-C	p. 163
Adjoints for Time-Dependent Optimal Control	p. 175
Development and First Applications of TAC++	p. 187
TAPENADE for C	p. 199
Coping with a Variable Number of Arguments when Transforming MATLAB Programs	p. 211
Code Optimization Techniques in Source Transformations for Interpreted Languages	p. 223
Automatic Sensitivity Analysis of DAE-systems Generated from Equation-Based Modeling Languages	p. 235
Index Determination in DAEs Using the Library indexdet and the ADOL-C Package for Algorithmic Differentiation	p. 247
Automatic Differentiation for GPU-Accelerated 2D/3D Registration	p. 259
Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab	p. 271
Toward Modular Multigrid Design Optimisation	p. 281
Large Electrical Power Systems Optimization Using Automatic Differentiation	p. 293
On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models	p. 303
Combinatorial Computation with Automatic Differentiation	p. 315
Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process	p. 327
Structure-Exploiting Automatic Differentiation of Finite Element Discretizations	p. 339
Large-Scale Transient Sensitivity Analysis of a Radiation-Damaged Bipolar Junction Transistor via Automatic Differentiation	p. 351
Table of Contents provided by Blackwell. All Rights Reserved.

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