A survey book focusing on the key relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers, as well as their applications. The key objective is to survey the field and present the recent developments. In doing so the topics covered shed light on a variety of perspectives. They reflect the mathematical aspects, such as the differentiation of iterative processes, and the analysis of nonsmooth code. They cover the scientific programming aspects, such as the use of adjoints in optimization and the propagation of rounding errors. They also cover "implementation" problems.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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parallel hierarchical approach, efficient adjoint code, parallel reversal schedules, elementary rounding errors, modern nonlinear regression, class fdtd, probing vectors, automatic differentiation tools, adjoint program, partially separable function, gear tooth flanks, adjoint variable method, linearisation schemes, adjoint statement, using automatic differentiation, water steam table, derivative code, gear tooth contact, computational differentiation, numerical toolkits, differentiated code, sparsity pattern, computational graph, algorithmic differentiation, map derivative Key Phrases - Capitalized Phrases (CAPs): (learn more)
Christèle Faure, Department of Energy, North Sea, Automatically Differentiating, Driven Cavity, Efficient Recomputation Algorithm, Mohamed Tadjouddine, Computational Sciences Division, Gauss Newton, Granularity Directional, Office of Advanced Scientific Computing Research New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
parallel hierarchical approach, efficient adjoint code, parallel reversal schedules, elementary rounding errors, modern nonlinear regression, class fdtd, probing vectors, automatic differentiation tools, adjoint program, partially separable function, gear tooth flanks, adjoint variable method, linearisation schemes, adjoint statement, using automatic differentiation, water steam table, derivative code, gear tooth contact, computational differentiation, numerical toolkits, differentiated code, sparsity pattern, computational graph, algorithmic differentiation, map derivative Key Phrases - Capitalized Phrases (CAPs): (learn more)
Christèle Faure, Department of Energy, North Sea, Automatically Differentiating, Driven Cavity, Efficient Recomputation Algorithm, Mohamed Tadjouddine, Computational Sciences Division, Gauss Newton, Granularity Directional, Office of Advanced Scientific Computing Research New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 54
books:
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58
books
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- Bifurcation and Chaos: Analysis, Algorithms, Applications (International Series of Numerical Mathematics) by R. Seydel in Back Matter (1), Back Matter (2), and Back Matter (3)
- Automatic Differentiation: Techniques and Applications (Lecture Notes in Computer Science) by Louis B. Rall in Back Matter (1), and Back Matter (2)
- Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics) by Andreas Griewank in Back Matter (1), and Back Matter (2)
- Automatic Differentiation of Algorithms: Theory, Implementation, and Application (Siam Proceedings Series) by Andreas Griewank in Back Matter (1), and Back Matter (2)
- Partial Differential Equations (Applied Mathematical Sciences) (v. 1) by Fritz John in Back Matter (1), and Back Matter (2)
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- Modeling Languages in Mathematical Optimization (Applied Optimization) by Josef Kallrath in Back Matter (1), Back Matter (2), and Back Matter (3)
- Automatic Differentiation: Applications, Theory, and Implementations (Lecture Notes in Computational Science and Engineering) by H. Martin Bücker in Back Matter (1), and Back Matter (2)
- Symbolic Algebraic Methods and Verification Methods (Springer Mathematics,) by Götz Alefeld on page 106, and page 126
- Accuracy and Stability of Numerical Algorithms by Nicholas J. Higham in Back Matter (1), and Back Matter (2)
- Power System Simulation by J.-P. Barret on page 267, and page 268
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