This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.
Author(s): Adrian Sandu (auth.), Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke (eds.)
Series: Lecture Notes in Computational Science and Engineering 64
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2008
Language: English
Pages: 368
City: Berlin; London
Tags: Computational Science and Engineering; Computational Mathematics and Numerical Analysis; Electrical Engineering; Mathematics of Computing
Front Matter....Pages I-XVIII
Reverse Automatic Differentiation of Linear Multistep Methods....Pages 1-12
Call Tree Reversal is NP-Complete....Pages 13-22
On Formal Certification of AD Transformations....Pages 23-33
Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation....Pages 35-44
A Modification of Weeks’ Method for Numerical Inversion of the Laplace Transform in the Real Case Based on Automatic Differentiation....Pages 45-54
A Low Rank Approach to Automatic Differentiation....Pages 55-65
Algorithmic Differentiation of Implicit Functions and Optimal Values....Pages 67-77
Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient....Pages 79-90
A Polynomial-Time Algorithm for Detecting Directed Axial Symmetry in Hessian Computational Graphs....Pages 91-102
On the Practical Exploitation of Scarsity....Pages 103-114
Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation....Pages 115-125
Efficient Higher-Order Derivatives of the Hypergeometric Function....Pages 127-137
The Diamant Approach for an Efficient Automatic Differentiation of the Asymptotic Numerical Method....Pages 139-149
Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals....Pages 151-161
Parallel Reverse Mode Automatic Differentiation for OpenMP Programs with ADOL-C....Pages 163-173
Adjoints for Time-Dependent Optimal Control....Pages 175-185
Development and First Applications of TAC++....Pages 187-197
TAPENADE for C....Pages 199-209
Coping with a Variable Number of Arguments when Transforming MATLAB Programs....Pages 211-222
Code Optimization Techniques in Source Transformations for Interpreted Languages....Pages 223-233
Automatic Sensitivity Analysis of DAE-systems Generated from Equation-Based Modeling Languages....Pages 235-246
Index Determination in DAEs Using the Library indexdet and the ADOL-C Package for Algorithmic Differentiation....Pages 247-257
Automatic Differentiation for GPU-Accelerated 2D/3D Registration....Pages 259-269
Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab....Pages 271-280
Toward Modular Multigrid Design Optimisation....Pages 281-291
Large Electrical Power Systems Optimization Using Automatic Differentiation....Pages 293-302
On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models....Pages 303-313
Combinatorial Computation with Automatic Differentiation....Pages 315-325
Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process....Pages 327-338
Structure-Exploiting Automatic Differentiation of Finite Element Discretizations....Pages 339-349
Large-Scale Transient Sensitivity Analysis of a Radiation-Damaged Bipolar Junction Transistor via Automatic Differentiation....Pages 351-362
Back Matter....Pages 363-368
