Recent Advances in Algorithmic Differentiation

Editors:

ISBN: 978-3-642-30022-6 (Print) 978-3-642-30023-3 (Online)

Table of contents (31 chapters)

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  1. Front Matter

    Pages i-xvii

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    Book Chapter

    Pages 1-9

    A Leibniz Notation for Automatic Differentiation

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    Book Chapter

    Pages 11-21

    Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics

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    Book Chapter

    Pages 23-33

    Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models

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    Book Chapter

    Pages 35-45

    Application of Automatic Differentiation to an Incompressible URANS Solver

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    Book Chapter

    Pages 47-57

    Applying Automatic Differentiation to the Community Land Model

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    Book Chapter

    Pages 59-69

    Using Automatic Differentiation to Study the Sensitivity of a Crop Model

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    Book Chapter

    Pages 71-81

    Efficient Automatic Differentiation of Matrix Functions

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    Book Chapter

    Pages 83-92

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

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    Book Chapter

    Pages 93-101

    Increasing Memory Locality by Executing Several Model Instances Simultaneously

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    Book Chapter

    Pages 103-113

    Adjoint Mode Computation of Subgradients for McCormick Relaxations

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    Book Chapter

    Pages 115-125

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

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    Book Chapter

    Pages 127-138

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

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    Book Chapter

    Pages 139-149

    On the Efficient Computation of Sparsity Patterns for Hessians

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    Book Chapter

    Pages 151-161

    Exploiting Sparsity in Automatic Differentiation on Multicore Architectures

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    Book Chapter

    Pages 163-173

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

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    Book Chapter

    Pages 175-185

    Connections Between Power Series Methods and Automatic Differentiation

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    Book Chapter

    Pages 187-196

    Hierarchical Algorithmic Differentiation A Case Study

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    Book Chapter

    Pages 197-207

    Storing Versus Recomputation on Multiple DAGs

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    Book Chapter

    Pages 209-219

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

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    Book Chapter

    Pages 221-231

    An Integer Programming Approach to Optimal Derivative Accumulation

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