Publication: Recent Advances in Algorithmic Differentiation
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Recent Advances in Algorithmic Differentiation

- Book -


Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther



AD Theory and Techniques

Contained Articles
- A Leibniz Notation for Automatic Differentiation
- AD in Fortran: Implementation via Prepreprocessor
- Adjoint Mode Computation of Subgradients for McCormick Relaxations
- An AD-Enabled Optimization ToolBox in LabVIEW™
- An Integer Programming Approach to Optimal Derivative Accumulation
- Application of Automatic Differentiation to an Incompressible URANS Solver
- Applying Automatic Differentiation to the Community Land Model
- Automatic Differentiation Through the Use of Hyper-Dual Numbers for Second Derivatives
- CasADI: A Symbolic Package for Automatic Differentiation and Optimal Control
- Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models
- Computing Derivatives in a Meshless Simulation Using Permutations in ADOL-C
- Connections Between Power Series Methods and Automatic Differentiation
- Efficient Automatic Differentiation of Matrix Functions
- Efficient Expression Templates for Operator Overloading-Based Automatic Differentiation
- Evaluating an Element of the Clarke Generalized Jacobian of a Piecewise Differentiable Function
- Exploiting Sparsity in Automatic Differentiation on Multicore Architectures
- Generative Programming for Automatic Differentiation
- Hierarchical Algorithmic Differentiation A Case Study
- High-Order Uncertainty Propagation Enabled by Computational Differentiation
- Implementation of Partial Separability in a Source-to-Source Transformation AD Tool
- Increasing Memory Locality by Executing Several Model Instances Simultaneously
- Java Automatic Differentiation Tool Using Virtual Operator Overloading
- Lazy K-Way Linear Combination Kernels for Efficient Runtime Sparse Jacobian Matrix Evaluations in C++
- Native Handling of Message-Passing Communication in Data-Flow Analysis
- On the Efficient Computation of Sparsity Patterns for Hessians
- Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics
- Storing Versus Recomputation on Multiple DAGs
- The Impact of Dynamic Data Reshaping on Adjoint Code Generation for Weakly-Typed Languages Such as Matlab
- The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set
- Using Automatic Differentiation to Study the Sensitivity of a Crop Model
- Using Directed Edge Separators to Increase Efficiency in the Determination of Jacobian Matrices via Automatic Differentiation

       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       title = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       ad_theotech = "General",
       year = "2012",
       volume = "87",
       doi = "10.1007/978-3-642-30023-3",
       isbn = "978-3-642-30022-6",
       issn = "1439-7358"


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