Automatic Differentiation: Applications, Theory, and Implementations: Lecture Notes in Computational Science and Engineering, cartea 50
Editat de H. Martin Bücker, George Corliss, Paul Hovland, Uwe Naumann, Boyana Norrisen Limba Engleză Paperback – 14 dec 2005
Din seria Lecture Notes in Computational Science and Engineering
- Preț: 375.63 lei
- 18% Preț: 1247.70 lei
- 18% Preț: 787.15 lei
- Preț: 384.31 lei
- 20% Preț: 990.95 lei
- 15% Preț: 648.56 lei
- 15% Preț: 653.00 lei
- Preț: 405.28 lei
- 18% Preț: 976.06 lei
- 18% Preț: 968.82 lei
- Preț: 397.97 lei
- 18% Preț: 962.49 lei
- 15% Preț: 647.08 lei
- 15% Preț: 648.56 lei
- 15% Preț: 649.54 lei
- 18% Preț: 1389.30 lei
- Preț: 428.30 lei
- 18% Preț: 1240.62 lei
- 20% Preț: 666.27 lei
- 15% Preț: 654.43 lei
- 15% Preț: 644.30 lei
- 18% Preț: 957.62 lei
- 18% Preț: 1224.18 lei
- 18% Preț: 904.11 lei
- 18% Preț: 1242.83 lei
- 20% Preț: 992.11 lei
- 15% Preț: 642.83 lei
- 18% Preț: 954.45 lei
- 18% Preț: 783.20 lei
- 18% Preț: 949.42 lei
- 15% Preț: 642.83 lei
- 18% Preț: 964.86 lei
- 18% Preț: 1260.83 lei
- 15% Preț: 650.37 lei
Preț: 1221.07 lei
Preț vechi: 1489.10 lei
-18% Nou
Puncte Express: 1832
Preț estimativ în valută:
233.71€ • 243.07$ • 195.85£
233.71€ • 243.07$ • 195.85£
Carte tipărită la comandă
Livrare economică 14-28 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783540284031
ISBN-10: 3540284036
Pagini: 400
Ilustrații: XVIII, 370 p. 108 illus.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.54 kg
Ediția:2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540284036
Pagini: 400
Ilustrații: XVIII, 370 p. 108 illus.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.54 kg
Ediția:2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Perspectives on Automatic Differentiation: Past, Present, and Future?.- Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities.- Solutions of ODEs with Removable Singularities.- Automatic Propagation of Uncertainties.- High-Order Representation of Poincarée Maps.- Computation of Matrix Permanent with Automatic Differentiation.- Computing Sparse Jacobian Matrices Optimally.- Application of AD-based Quasi-Newton Methods to Stiff ODEs.- Reduction of Storage Requirement by Checkpointing for Time-Dependent Optimal Control Problems in ODEs.- Improving the Performance of the Vertex Elimination Algorithm for Derivative Calculation.- Flattening Basic Blocks.- The Adjoint Data-Flow Analyses: Formalization, Properties, and Applications.- Semiautomatic Differentiation for Efficient Gradient Computations.- Computing Adjoints with the NAGWare Fortran 95 Compiler.- Transforming Equation-Based Models in Process Engineering.- Extension of TAPENADE toward Fortran 95.- A Macro Language for Derivative Definition in ADiMat.- Simulation and Optimization of the Tevatron Accelerator.- Periodic Orbits of Hybrid Systems and Parameter Estimation via AD.- Implementation of Automatic Differentiation Tools for Multicriteria IMRT Optimization.- Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization.- Automatic Differentiation: A Tool for Variational Data Assimilation and Adjoint Sensitivity Analysis for Flood Modeling.- Development of an Adjoint for a Complex Atmospheric Model, the ARPS, using TAF.- Tangent Linear and Adjoint Versions of NASA/GMAO’s Fortran 90 Global Weather Forecast Model.- Efficient Sensitivities for the Spin-Up Phase.- Streamlined Circuit Device Model Development with fREEDAR® ãnd ADOL-C.- Adjoint Differentiation of aStructural Dynamics Solver.- A Bibliography of Automatic Differentiation.
Caracteristici
Includes supplementary material: sn.pub/extras