Automatic Differentiation: Applications, Theory, and Implementations

ISBN: 978-3-540-28403-1 (Print) 978-3-540-28438-3 (Online)

Table of contents (28 chapters)

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    Book Chapter

    Pages 1-14

    Perspectives on Automatic Differentiation: Past, Present, and Future?

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    Pages 15-34

    Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities

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    Pages 35-45

    Solutions of ODEs with Removable Singularities

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    Pages 47-58

    Automatic Propagation of Uncertainties

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    Pages 59-66

    High-Order Representation of Poincarée Maps

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    Pages 67-76

    Computation of Matrix Permanent with Automatic Differentiation

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    Pages 77-87

    Computing Sparse Jacobian Matrices Optimally

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    Pages 89-98

    Application of AD-based Quasi-Newton Methods to Stiff ODEs

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    Pages 99-110

    Reduction of Storage Requirement by Checkpointing for Time-Dependent Optimal Control Problems in ODEs

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    Pages 111-120

    Improving the Performance of the Vertex Elimination Algorithm for Derivative Calculation

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    Pages 121-133

    Flattening Basic Blocks

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    Pages 135-146

    The Adjoint Data-Flow Analyses: Formalization, Properties, and Applications

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    Pages 147-158

    Semiautomatic Differentiation for Efficient Gradient Computations

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    Pages 189-198

    Transforming Equation-Based Models in Process Engineering

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    Pages 159-169

    Computing Adjoints with the NAGWare Fortran 95 Compiler

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    Pages 171-179

    Extension of TAPENADE toward Fortran 95

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    Pages 181-188

    A Macro Language for Derivative Definition in ADiMat

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    Pages 199-209

    Simulation and Optimization of the Tevatron Accelerator

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    Pages 211-223

    Periodic Orbits of Hybrid Systems and Parameter Estimation via AD

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    Book Chapter

    Pages 225-234

    Implementation of Automatic Differentiation Tools for Multicriteria IMRT Optimization

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