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Automatic Differentiation: Applications, Theory, and Implementations 2006 ed. [Minkštas viršelis]

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  • Formatas: Paperback / softback, 370 pages, aukštis x plotis: 235x155 mm, weight: 1190 g, 108 Illustrations, black and white; XVIII, 370 p. 108 illus., 1 Paperback / softback
  • Serija: Lecture Notes in Computational Science and Engineering 50
  • Išleidimo metai: 14-Dec-2005
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540284036
  • ISBN-13: 9783540284031
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 370 pages, aukštis x plotis: 235x155 mm, weight: 1190 g, 108 Illustrations, black and white; XVIII, 370 p. 108 illus., 1 Paperback / softback
  • Serija: Lecture Notes in Computational Science and Engineering 50
  • Išleidimo metai: 14-Dec-2005
  • Leidėjas: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540284036
  • ISBN-13: 9783540284031
Kitos knygos pagal šią temą:
The Fourth International Conference on Automatic Di erentiation was held July20-23inChicago,Illinois.Theconferenceincludedaonedayshortcourse, 42 presentations, and a workshop for tool developers. This gathering of au- matic di erentiation researchers extended a sequence that began in Breck- ridge, Colorado, in 1991 and continued in Santa Fe, New Mexico, in 1996 and Nice, France, in 2000. We invited conference participants and the general - tomatic di erentiation community to submit papers to this special collection. The28acceptedpapersre ectthestateoftheartinautomaticdi erentiation. The number of automatic di erentiation tools based on compiler techn- ogy continues to expand. The papers in this volume discuss the implem- tation and application of several compiler-based tools for Fortran, including the venerable ADIFOR, an extended NAGWare compiler, TAF, and TAPE- NADE. While great progress has been made toward robust, compiler-based tools for C/C++, most notably in the form of the ADIC and TAC++ tools, for now operator-overloading tools such as ADOL-C remain the undisputed champions for reverse-mode automatic di erentiation of C++. Tools for - tomatic di erentiation of high level languages, including COSY and ADiMat, continue to grow in importance as the productivity gains o? ered by high-level programming are recognized.
Perspectives on Automatic Differentiation: Past, Present, and Future?
1(14)
Louis B. Rall
Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities
15(20)
Paul J. Werbos
Solutions of ODEs with Removable Singularities
35(12)
Harley Flanders
Automatic Propagation of Uncertainties
47(12)
Bruce Christianson
Maurice Cox
High-Order Representation of Poincare Maps
59(8)
Johannes Grote
Martin Berz
Kyoko Makino
Computation of Matrix Permanent with Automatic Differentiation
67(10)
Koichi Kubota
Computing Sparse Jacobian Matrices Optimally
77(12)
Shahadat Hossain
Trond Steihaug
Application of AD-based Quasi-Newton Methods to Stiff ODEs
89(10)
Sebastian Schlenkrich
Andrea Walther
Andreas Griewank
Reduction of Storage Requirement by Checkpointing for Time-Dependent Optimal Control Problems in ODEs
99(12)
Julia Sternberg
Andreas Griewank
Improving the Performance of the Vertex Elimination Algorithm for Derivative Calculation
111(10)
M. Tadjouddine
F. Bodman
J. D. Pryce
S. A. Forth
Flattening Basic Blocks
121(14)
Jean Utke
The Adjoint Data-Flow Analyses: Formalization, Properties, and Applications
135(12)
Laurent Hascoet
Mauricio Araya-Polo
Semiautomatic Differentiation for Efficient Gradient Computations
147(12)
David M. Gay
Computing Adjoints with the NAGWare Fortran 95 Compiler
159(12)
Uwe Naumann
Jan Riehme
Extension of Tapenade toward Fortran 95
171(10)
Valerie Pascual
Laurent Hascoet
A Macro Language for Derivative Definition in ADiMat
181(8)
Christian H. Bischof
H. Martin Bucker
Andre Vehreschild
Transforming Equation-Based Models in Process Engineering
189(10)
Christian H. Bischof
H. Martin Bucker
Wolfgang Marquardt
Monika Petera
Jutta Wyes
Simulation and Optimization of the Tevatron Accelerator
199(12)
Pavel Snopok
Carol Johnstone
Martin Berz
Periodic Orbits of Hybrid Systems and Parameter Estimation via AD
211(14)
Eric Phipps
Richard Casey
John Guckenheimer
Implementation of Automatic Differentiation Tools for Multicriteria IMRT Optimization
225(10)
Kyung-Wook Jee
Daniel L. McShan
Benedick A. Fraass
Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization
235(14)
Derya B. Ozyurt
Paul I. Barton
Automatic Differentiation: A Tool for Variational Data Assimilation and Adjoint Sensitivity Analysis for Flood Modeling
249(14)
W. Castaings
D. Dartus
M. Honnorat
F. -X. Le Dimet
Y. Loukili
J. Monnier
Development of an Adjoint for a Complex Atmospheric Model, the ARPS, using TAF
263(12)
Ying Xiao
Ming Xue
William Martin
Jidong Gao
Tangent Linear and Adjoint Versions of NASA/GMAO's Fortran 90 Global Weather Forecast Model
275(10)
Ralf Giering
Thomas Kaminski
Ricardo Todling
Ronald Errico
Ronald Gelaro
Nathan Winslow
Efficient Sensitivities for the Spin-Up Phase
285(10)
Thomas Kaminski
Ralf Giering
Michael Voßbeck
Streamlined Circuit Device Model Development with fREEDA® and ADOL-C
295(14)
Frank P. Hart
Nikhil Kriplani
Sonali R. Luniya
Carlos E. Christoffersen
Michael B. Steer
Adjoint Differentiation of a Structural Dynamics Solver
309(12)
Mohamed Tadjouddine
Shaun A. Forth
Andy J. Keane
A Bibliography of Automatic Differentiation
321(2)
H. Martin Bucker
George F. Corliss
References 323(32)
Index 355