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corliss g automatic differentiation algorithms 2002

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Textbook in PDF format Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development. Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). A strong theme of the book is the relationships between AD tools and other software tools, such as compilers and parallelizers. A rich variety of significant applications are presented as well, including optimum-shape design problems, for which AD offers more efficient tools and techniques. Front Matter Front Matter Differentiation Methods for Industrial Strength Problems Automatic Differentiation Tools in Optimization Software Using Automatic Differentiation for SecondOrder Matrixfree Methods in PDEconstrained Optimization Performance Issues in Automatic Differentiation on Superscalar Processors Present and Future Scientific Computation Environments Front Matter A Case Study of Computational Differentiation Applied to Neutron Scattering Odyssée versus Hand Differentiation of a Terrain Modeling Application Automatic Differentiation for Modern Nonlinear Regression Sensitivity Analysis and Parameter Tuning of a SeaIce Model Electron Paramagnetic Resonance, Optimization and Automatic Differentiation Front Matter Continuous Optimal Control Sensitivity Analysis with AD Application of Automatic Differentiation to Race Car Performance Optimisation Globalization of Pantoja’s Optimal Control Algorithm Analytical Aspects and Practical Pitfalls in Technical Applications of AD Nonlinear Observer Design Using Automatic Differentiation Front Matter On the Iterative Solution of Adjoint Equations Aerofoil Optimisation via AD of a Multigrid CellVertex Euler Flow Solver Automatic Differentiation and the Adjoint State Method Efficient Operator Overloading AD for Solving Nonlinear PDEs Integrating AD with ObjectOriented Toolkits for HighPerformance Scientific Computing Front Matter Optimal Sizing of Industrial Structural Mechanics Problems Using AD Second Order Exact Derivatives to Perform Optimization on SelfConsistent Integral Equations Problems Accurate Gear Tooth Contact and Sensitivity Computation for Hypoid Bevel Gears Optimal Laser Control of Chemical Reactions Using AD Front Matter Automatically Differentiating MPI Datatypes: The Complete Story Sensitivity Analysis Using Parallel ODE Solvers and Automatic Differentiation in C: SensPVODE and ADIC A Parallel Hierarchical Approach for Automatic Differentiation New Results on Program Reversals Front Matter Elimination Techniques for Cheap Jacobians AD Tools and Prospects for Optimal AD in CFD Flux Jacobian Calculations Reducing the Number of AD Passes for Computing a Sparse Jacobian Matrix Verifying Jacobian Sparsity Front Matter Recomputations in Reverse Mode AD Minimizing the Tape Size Adjoining Independent Computations Complexity Analysis of Automatic Differentiation in the Hyperion Software Expression Templates and Forward Mode Automatic Differentiation Front Matter Application of AD to a Family of Periodic Functions FAD Method to Compute Second Order Derivatives Application of Higher Order Derivatives to Parameterization Front Matter Efficient HighOrder Methods for ODEs and DAEs Front Matter From Rounding Error Estimation to Automatic Correction with Automatic Differentiation New Applications of Taylor Model Methods Taylor Models in Deterministic Global Optimization Towards a Universal Data Type for Scientific Computing Back Matter

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corliss g automatic differentiation algorithms 2002