The Resource Recent advances in algorithmic differentiation, Shaun Forth...[et al.], editors, (electronic resource)

Recent advances in algorithmic differentiation, Shaun Forth...[et al.], editors, (electronic resource)

Label
Recent advances in algorithmic differentiation
Title
Recent advances in algorithmic differentiation
Statement of responsibility
Shaun Forth...[et al.], editors
Creator
Contributor
Provider
Subject
Genre
Language
eng
Summary
The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD). The 31 contributed papers presented at the AD2012 conference cover the application of AD to many areas in science and engineering as well as aspects of AD theory and its implementation in tools. For all papers the referees, selected from the program committee and the greater community, as well as the editors have emphasized accessibility of the presented ideas also to non-AD experts. In the AD tools arena new implementations are introduced covering, for example, Java and graphical modeling environments or join the set of existing tools for Fortran. New developments in AD algorithms target the efficiency of matrix-operation derivatives, detection and exploitation of sparsity, partial separability, the treatment of nonsmooth functions, and other high-level mathematical aspects of the numerical computations to be differentiated. Applications stem from the Earth sciences, nuclear engineering, fluid dynamics, and chemistry, to name just a few. In many cases the applications in a given area of science or engineering share characteristics that require specific approaches to enable AD capabilities or provide an opportunity for efficiency gains in the derivative computation. The description of these characteristics and of the techniques for successfully using AD should make the proceedings a valuable source of information for users of AD tools
Member of
Cataloging source
GW5XE
Image bit depth
0
LC call number
QA304
LC item number
.I58 2012
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2012
http://bibfra.me/vocab/lite/meetingName
International Conference on Automatic Differentiation
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • SpringerLink
  • Forth, Shaun
Series statement
Lecture Notes in Computational Science and Engineering,
Series volume
87
http://library.link/vocab/subjectName
  • Algorithms
  • Electronic Data Processing
  • Mathematics
  • Differential calculus
  • Differential-difference equations
Label
Recent advances in algorithmic differentiation, Shaun Forth...[et al.], editors, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references
Color
not applicable
Contents
  • Applying Automatic Differentiation to the Community Land Model
  • Azamat Mametjanov, Boyana Norris, Xiaoyan Zeng, Beth Drewniak and Jean Utke, et al.
  • Using Automatic Differentiation to Study the Sensitivity of a Crop Model
  • Claire Lauvernet, Laurent Hascoët, François-Xavier Le Dimet and Frédéric Baret
  • Efficient Automatic Differentiation of Matrix Functions
  • Peder A. Olsen, Steven J. Rennie and Vaibhava Goel
  • Native Handling of Message-Passing Communication in Data-Flow Analysis
  • Valérie Pascual and Laurent Hascoët
  • Increasing Memory Locality by Executing Several Model Instances Simultaneously
  • Ralf Giering and Michael Vo{szlig}beck
  • A Leibniz Notation for Automatic Differentiation
  • Adjoint Mode Computation of Subgradients for McCormick Relaxations
  • Markus Beckers, Viktor Mosenkis and Uwe Naumann
  • Evaluating an Element of the Clarke Generalized Jacobian of a Piecewise Differentiable Function
  • Kamil A. Khan and Paul I. Barton
  • The Impact of Dynamic Data Reshaping on Adjoint Code Generation for Weakly-Typed Languages Such as Matlab
  • Johannes Willkomm, Christian H. Bischof and H. Martin Bücker
  • On the Efficient Computation of Sparsity Patterns for Hessians
  • Andrea Walther
  • Exploiting Sparsity in Automatic Differentiation on Multicore Architectures
  • Benjamin Letschert, Kshitij Kulshreshtha, Andrea Walther, Duc Nguyen and Assefaw Gebremedhin, et al.
  • Bruce Christianson
  • Automatic Differentiation Through the Use of Hyper-Dual Numbers for Second Derivatives
  • Jeffrey A. Fike and Juan J. Alonso
  • Connections Between Power Series Methods and Automatic Differentiation
  • David C. Carothers, Stephen K. Lucas, G. Edgar Parker, Joseph D. Rudmin and James S. Sochacki, et al.
  • Hierarchical Algorithmic Differentiation A Case Study
  • Johannes Lotz, Uwe Naumann and Jörn Ungermann
  • Storing Versus Recomputation on Multiple DAGs
  • Heather Cole-Mullen, Andrew Lyons and Jean Utke
  • Using Directed Edge Separators to Increase Efficiency in the Determination of Jacobian Matrices via Automatic Differentiation
  • Thomas F. Coleman, Xin Xiong and Wei Xu
  • Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics
  • An Integer Programming Approach to Optimal Derivative Accumulation
  • Jieqiu Chen, Paul Hovland, Todd Munson and Jean Utke
  • The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set
  • Torsten Bosse and Andreas Griewank
  • Java Automatic Differentiation Tool Using Virtual Operator Overloading
  • Phuong Pham-Quang and Benoit Delinchant
  • High-Order Uncertainty Propagation Enabled by Computational Differentiation
  • Ahmad Bani Younes, James Turner, Manoranjan Majji and John Junkins
  • Generative Programming for Automatic Differentiation
  • Marco Nehmeier
  • Daniel R. Reynolds and Ravi Samtaney
  • AD in Fortran: Implementation via Prepreprocessor
  • Alexey Radul, Barak A. Pearlmutter and Jeffrey Mark Siskind
  • An AD-Enabled Optimization ToolBox in LabVIEWTM
  • Abhishek Kr. Gupta and Shaun A. Forth
  • CasADi: A Symbolic Package for Automatic Differentiation and Optimal Control
  • Joel Andersson, Johan Åkesson and Moritz Diehl
  • Efficient Expression Templates for Operator Overloading-Based Automatic Differentiation
  • Eric Phipps and Roger Pawlowski
  • Computing Derivatives in a Meshless Simulation Using Permutations in ADOL-C
  • Kshitij Kulshreshtha and Jan Marburger
  • Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models
  • Lazy K-Way Linear Combination Kernels for Efficient Runtime Sparse Jacobian Matrix Evaluations in C++
  • Rami M. Younis and Hamdi A. Tchelepi
  • Implementation of Partial Separability in a Source-to-Source Transformation AD Tool
  • Sri Hari Krishna Narayanan, Boyana Norris, Paul Hovland and Assefaw Gebremedhin
  • James A. Reed, Jean Utke and Hany S. Abdel-Khalik
  • Application of Automatic Differentiation to an Incompressible URANS Solver
  • Emre Özkaya, Anil Nemili and Nicolas R. Gauger
Dimensions
unknown
Extent
1 online resource.
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783642300226
Level of compression
uncompressed
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (OCoLC)806228793
  • (OCoLC)ocn806228793
Label
Recent advances in algorithmic differentiation, Shaun Forth...[et al.], editors, (electronic resource)
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references
Color
not applicable
Contents
  • Applying Automatic Differentiation to the Community Land Model
  • Azamat Mametjanov, Boyana Norris, Xiaoyan Zeng, Beth Drewniak and Jean Utke, et al.
  • Using Automatic Differentiation to Study the Sensitivity of a Crop Model
  • Claire Lauvernet, Laurent Hascoët, François-Xavier Le Dimet and Frédéric Baret
  • Efficient Automatic Differentiation of Matrix Functions
  • Peder A. Olsen, Steven J. Rennie and Vaibhava Goel
  • Native Handling of Message-Passing Communication in Data-Flow Analysis
  • Valérie Pascual and Laurent Hascoët
  • Increasing Memory Locality by Executing Several Model Instances Simultaneously
  • Ralf Giering and Michael Vo{szlig}beck
  • A Leibniz Notation for Automatic Differentiation
  • Adjoint Mode Computation of Subgradients for McCormick Relaxations
  • Markus Beckers, Viktor Mosenkis and Uwe Naumann
  • Evaluating an Element of the Clarke Generalized Jacobian of a Piecewise Differentiable Function
  • Kamil A. Khan and Paul I. Barton
  • The Impact of Dynamic Data Reshaping on Adjoint Code Generation for Weakly-Typed Languages Such as Matlab
  • Johannes Willkomm, Christian H. Bischof and H. Martin Bücker
  • On the Efficient Computation of Sparsity Patterns for Hessians
  • Andrea Walther
  • Exploiting Sparsity in Automatic Differentiation on Multicore Architectures
  • Benjamin Letschert, Kshitij Kulshreshtha, Andrea Walther, Duc Nguyen and Assefaw Gebremedhin, et al.
  • Bruce Christianson
  • Automatic Differentiation Through the Use of Hyper-Dual Numbers for Second Derivatives
  • Jeffrey A. Fike and Juan J. Alonso
  • Connections Between Power Series Methods and Automatic Differentiation
  • David C. Carothers, Stephen K. Lucas, G. Edgar Parker, Joseph D. Rudmin and James S. Sochacki, et al.
  • Hierarchical Algorithmic Differentiation A Case Study
  • Johannes Lotz, Uwe Naumann and Jörn Ungermann
  • Storing Versus Recomputation on Multiple DAGs
  • Heather Cole-Mullen, Andrew Lyons and Jean Utke
  • Using Directed Edge Separators to Increase Efficiency in the Determination of Jacobian Matrices via Automatic Differentiation
  • Thomas F. Coleman, Xin Xiong and Wei Xu
  • Sparse Jacobian Construction for Mapped Grid Visco-Resistive Magnetohydrodynamics
  • An Integer Programming Approach to Optimal Derivative Accumulation
  • Jieqiu Chen, Paul Hovland, Todd Munson and Jean Utke
  • The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set
  • Torsten Bosse and Andreas Griewank
  • Java Automatic Differentiation Tool Using Virtual Operator Overloading
  • Phuong Pham-Quang and Benoit Delinchant
  • High-Order Uncertainty Propagation Enabled by Computational Differentiation
  • Ahmad Bani Younes, James Turner, Manoranjan Majji and John Junkins
  • Generative Programming for Automatic Differentiation
  • Marco Nehmeier
  • Daniel R. Reynolds and Ravi Samtaney
  • AD in Fortran: Implementation via Prepreprocessor
  • Alexey Radul, Barak A. Pearlmutter and Jeffrey Mark Siskind
  • An AD-Enabled Optimization ToolBox in LabVIEWTM
  • Abhishek Kr. Gupta and Shaun A. Forth
  • CasADi: A Symbolic Package for Automatic Differentiation and Optimal Control
  • Joel Andersson, Johan Åkesson and Moritz Diehl
  • Efficient Expression Templates for Operator Overloading-Based Automatic Differentiation
  • Eric Phipps and Roger Pawlowski
  • Computing Derivatives in a Meshless Simulation Using Permutations in ADOL-C
  • Kshitij Kulshreshtha and Jan Marburger
  • Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models
  • Lazy K-Way Linear Combination Kernels for Efficient Runtime Sparse Jacobian Matrix Evaluations in C++
  • Rami M. Younis and Hamdi A. Tchelepi
  • Implementation of Partial Separability in a Source-to-Source Transformation AD Tool
  • Sri Hari Krishna Narayanan, Boyana Norris, Paul Hovland and Assefaw Gebremedhin
  • James A. Reed, Jean Utke and Hany S. Abdel-Khalik
  • Application of Automatic Differentiation to an Incompressible URANS Solver
  • Emre Özkaya, Anil Nemili and Nicolas R. Gauger
Dimensions
unknown
Extent
1 online resource.
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783642300226
Level of compression
uncompressed
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (OCoLC)806228793
  • (OCoLC)ocn806228793

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