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Order by: [Title], [Author], [Editor], [Year]
Bruce Christianson
A Leibniz Notation for Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
General
Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind
AD in Fortran: Implementation via Prepreprocessor
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
Farfallen
Theory & Techniques:
Implementation Strategies
Markus Beckers, Viktor Mosenkis, Uwe Naumann
Adjoint Mode Computation of Subgradients for McCormick Relaxations
Recent Advances in Algorithmic Differentiation, Springer, 2012
not yet classified
Abhishek Kr. Gupta, Shaun A. Forth
An AD-Enabled Optimization ToolBox in LabVIEW™
Recent Advances in Algorithmic Differentiation, Springer, 2012
not yet classified
Jieqiu Chen, Paul Hovland, Todd Munson, Jean Utke
An Integer Programming Approach to Optimal Derivative Accumulation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Computational Graph
Emre Özkaya, Anil Nemili, Nicolas R. Gauger
Application of Automatic Differentiation to an Incompressible URANS Solver
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Adjoint, Parallelism
Azamat Mametjanov, Boyana Norris, Xiaoyan Zeng, Beth Drewniak, Jean Utke, Mihai Anitescu, Paul Hovland
Applying Automatic Differentiation to the Community Land Model
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
OpenAD
Jeffrey A. Fike, Juan J. Alonso
Automatic Differentiation Through the Use of Hyper-Dual Numbers for Second Derivatives
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Hessian
Joel Andersson, Johan \AAkesson, Moritz Diehl
CasADI: A Symbolic Package for Automatic Differentiation and Optimal Control
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
CasADI
James A. Reed, Jean Utke, Hany S. Abdel-Khalik
Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models
Recent Advances in Algorithmic Differentiation, Springer, 2012
Application Area:
Uncertainty Analysis
Tools:
OpenAD, Rapsodia
Theory & Techniques:
Higher Order, Reverse Mode
Kshitij Kulshreshtha, Jan Marburger
Computing Derivatives in a Meshless Simulation Using Permutations in ADOL-C
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
ADOL-C
David C. Carothers, Stephen K. Lucas, G. Edgar Parker, Joseph D. Rudmin, James S. Sochacki, Roger J. Thelwell, Anthony Tongen, Paul G. Warne
Connections Between Power Series Methods and Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
General
Peder A. Olsen, Steven J. Rennie, Vaibhava Goel
Efficient Automatic Differentiation of Matrix Functions
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Hierarchical Approach
Eric Phipps, Roger Pawlowski
Efficient Expression Templates for Operator Overloading-Based Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
Sacado
Theory & Techniques:
Code Optimization
Kamil A. Khan, Paul I. Barton
Evaluating an Element of the Clarke Generalized Jacobian of a Piecewise Differentiable Function
Recent Advances in Algorithmic Differentiation, Springer, 2012
not yet classified
Benjamin Letschert, Kshitij Kulshreshtha, Andrea Walther, Duc Nguyen, Assefaw Gebremedhin, Alex Pothen
Exploiting Sparsity in Automatic Differentiation on Multicore Architectures
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Parallelism, Sparsity
Marco Nehmeier
Generative Programming for Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Code Optimization
Johannes Lotz, Uwe Naumann, Jörn Ungermann
Hierarchical Algorithmic Differentiation A Case Study
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Hierarchical Approach
Ahmad Bani Younes, James Turner, Manoranjan Majji, John Junkins
High-Order Uncertainty Propagation Enabled by Computational Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
Higher Order, Uncertainties
Sri Hari Krishna Narayanan, Boyana Norris, Paul Hovland, Assefaw Gebremedhin
Implementation of Partial Separability in a Source-to-Source Transformation AD Tool
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
partial separability

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