Advances in Automatic Differentiation [electronic resource] / edited by Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke.
Contributor(s): Bischof, Christian H [editor.] | Bücker, H. Martin [editor.] | Hovland, Paul [editor.] | Naumann, Uwe [editor.] | Utke, Jean [editor.] | SpringerLink (Online service)Material type: TextSeries: Lecture Notes in Computational Science and Engineering: 64Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: XVIII, 368 p. 111 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540689423Subject(s): Mathematics | Computers | Computer science -- Mathematics | Computer mathematics | Mathematical optimization | Electrical engineering | Mathematics | Computational Science and Engineering | Optimization | Theory of Computation | Computational Mathematics and Numerical Analysis | Electrical Engineering | Mathematics of ComputingAdditional physical formats: Printed edition:: No titleDDC classification: 004 LOC classification: QA71-90Online resources: Click here to access online
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Reverse Automatic Differentiation of Linear Multistep Methods -- Call Tree Reversal is NP-Complete -- On Formal Certification of AD Transformations -- Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation -- A Modification of Weeks’ Method for Numerical Inversion of the Laplace Transform in the Real Case Based on Automatic Differentiation -- A Low Rank Approach to Automatic Differentiation -- Algorithmic Differentiation of Implicit Functions and Optimal Values -- Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient -- A Polynomial-Time Algorithm for Detecting Directed Axial Symmetry in Hessian Computational Graphs -- On the Practical Exploitation of Scarsity -- Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation -- Efficient Higher-Order Derivatives of the Hypergeometric Function -- The Diamant Approach for an Efficient Automatic Differentiation of the Asymptotic Numerical Method -- Tangent-on-Tangent vs. Tangent-on-Reverse for Second Differentiation of Constrained Functionals -- Parallel Reverse Mode Automatic Differentiation for OpenMP Programs with ADOL-C -- Adjoints for Time-Dependent Optimal Control -- Development and First Applications of TAC++ -- TAPENADE for C -- Coping with a Variable Number of Arguments when Transforming MATLAB Programs -- Code Optimization Techniques in Source Transformations for Interpreted Languages -- Automatic Sensitivity Analysis of DAE-systems Generated from Equation-Based Modeling Languages -- Index Determination in DAEs Using the Library indexdet and the ADOL-C Package for Algorithmic Differentiation -- Automatic Differentiation for GPU-Accelerated 2D/3D Registration -- Robust Aircraft Conceptual Design Using Automatic Differentiation in Matlab -- Toward Modular Multigrid Design Optimisation -- Large Electrical Power Systems Optimization Using Automatic Differentiation -- On the Application of Automatic Differentiation to the Likelihood Function for Dynamic General Equilibrium Models -- Combinatorial Computation with Automatic Differentiation -- Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process -- Structure-Exploiting Automatic Differentiation of Finite Element Discretizations -- Large-Scale Transient Sensitivity Analysis of a Radiation-Damaged Bipolar Junction Transistor via Automatic Differentiation.
This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.