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Advances in Automatic Differentiation

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Cover of 'Advances in Automatic Differentiation'

Table of Contents

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    Book Overview
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    Chapter 1 Reverse Automatic Differentiation of Linear Multistep Methods
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    Chapter 2 Call Tree Reversal is NP-Complete
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    Chapter 3 On Formal Certification of AD Transformations
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    Chapter 4 Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation
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    Chapter 5 A Modification of Weeks’ Method for Numerical Inversion of the Laplace Transform in the Real Case Based on Automatic Differentiation
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    Chapter 6 A Low Rank Approach to Automatic Differentiation
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    Chapter 7 Algorithmic Differentiation of Implicit Functions and Optimal Values
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    Chapter 8 Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient
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    Chapter 9 A Polynomial-Time Algorithm for Detecting Directed Axial Symmetry in Hessian Computational Graphs
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    Chapter 10 On the Practical Exploitation of Scarsity
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    Chapter 11 Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation
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    Chapter 12 Efficient Higher-Order Derivatives of the Hypergeometric Function
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    Chapter 13 The Diamant Approach for an Efficient Automatic Differentiation of the Asymptotic Numerical Method
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    Chapter 14 Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals
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    Chapter 15 Parallel Reverse Mode Automatic Differentiation for OpenMP Programs with ADOL-C
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    Chapter 16 Adjoints for Time-Dependent Optimal Control
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    Chapter 17 Development and First Applications of TAC++
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    Chapter 18 TAPENADE for C
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    Chapter 19 Coping with a Variable Number of Arguments when Transforming MATLAB Programs
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    Chapter 20 Code Optimization Techniques in Source Transformations for Interpreted Languages
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    Chapter 21 Automatic Sensitivity Analysis of DAE-systems Generated from Equation-Based Modeling Languages
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    Chapter 22 Index Determination in DAEs Using the Library indexdet and the ADOL-C Package for Algorithmic Differentiation
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    Chapter 23 Automatic Differentiation for GPU-Accelerated 2D/3D Registration
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    Chapter 24 Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab
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    Chapter 25 Toward Modular Multigrid Design Optimisation
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    Chapter 26 Large Electrical Power Systems Optimization Using Automatic Differentiation
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    Chapter 27 On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models
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    Chapter 28 Combinatorial Computation with Automatic Differentiation
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    Chapter 29 Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process
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    Chapter 30 Structure-Exploiting Automatic Differentiation of Finite Element Discretizations
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    Chapter 31 Large-Scale Transient Sensitivity Analysis of a Radiation-Damaged Bipolar Junction Transistor via Automatic Differentiation
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Title
Advances in Automatic Differentiation
Published by
Springer, Berlin, Heidelberg, August 2008
DOI 10.1007/978-3-540-68942-3
ISBNs
978-3-54-068935-5, 978-3-54-068942-3
Editors

Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Switzerland 1 3%
United Kingdom 1 3%
Norway 1 3%
Romania 1 3%
Unknown 29 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 41%
Student > Master 6 18%
Researcher 4 12%
Student > Doctoral Student 3 9%
Unspecified 3 9%
Other 4 12%
Readers by discipline Count As %
Engineering 13 38%
Computer Science 7 21%
Unspecified 4 12%
Physics and Astronomy 3 9%
Earth and Planetary Sciences 3 9%
Other 4 12%