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Order by: [Title], [Author], [Editor], [Year]
Filip Srajer, Zuzana Kukelova, Andrew Fitzgibbon
A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Bruce Christianson
A Leibniz Notation for Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
Theory & Techniques:
General
Lisa Kusch, Tim Albring, Andrea Walther, Nicolas R. Gauger
A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Alexander Hück, Christian Bischof, Max Sagebaum, Nicolas R. Gauger, Benjamin Jurgelucks, Eric Larour, Gilberto Perez
A usability case study of algorithmic differentiation tools on the ISSM ice sheet model
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
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
Sabrina Fiege, Andrea Walther, Kshitij Kulshreshtha, Andreas Griewank
Algorithmic differentiation for piecewise smooth functions: a case study for robust optimization
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
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
Isabelle Charpentier, Jens Gustedt
Arbogast: Higher order automatic differentiation for special functions with Modular C
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
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
Kamil A. Khan
Branch-locking AD techniques for nonsmooth composite functions and nonsmooth implicit functions
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Theory & Techniques:
Generalized Jacobian, Reverse Mode
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
Richard D. Neidinger, Benjamin Altman
Comparing high-order multivariate AD methods
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Paul I. Barton, Kamil A. Khan, Peter Stechlinski, Harry A. J. Watson
Computationally relevant generalized derivatives: theory, evaluation and applications
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Theory & Techniques:
Generalized Jacobian
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

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