Andreas Griewank, Andrea Walther

Evaluating Derivatives

Principles and Techniques of Algorithmic Differentiation

• Broschiertes Buch

Produktbeschreibung

This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity. There is also added material on checkpointing and iterative differentiation.

Produktdetails

• Verlag: Society for Industrial & Applied Mathematics, U.S.
• 2 Revised edition
• Seitenzahl: 460
• Erscheinungstermin: 6. November 2008
• Englisch
• Abmessung: 256mm x 177mm x 25mm
• Gewicht: 986g
• ISBN-13: 9780898716597
• ISBN-10: 0898716594
• Artikelnr.: 27086316

Autorenporträt

Andreas Griewank is a former senior scientist of Argonne National Laboratory and authored the first edition of this book in 2000. He holds a Ph.D. from the Australian National University and is currently Deputy Director of the Institute of Mathematics at Humboldt University Berlin and a member of the DFG Research Center Matheon, Mathematics for Key Technologies. His main research interests are nonlinear optimization and scientific computing.

Inhaltsangabe

Rules
Preface
Prologue
Mathematical symbols
1. Introduction
2. A framework for evaluating functions
3. Fundamentals of forward and reverse
4. Memory issues and complexity bounds
5. Repeating and extending reverse
6. Implementation and software
7. Sparse forward and reverse
8. Exploiting sparsity by compression
9. Going beyond forward and reverse
10. Jacobian and Hessian accumulation
11. Observations on efficiency
12. Reversal schedules and checkpointing
13. Taylor and tensor coefficients
14. Differentiation without differentiability
15. Implicit and iterative differentiation
Epilogue
List of figures
List of tables
Assumptions and definitions
Propositions, corollaries, and lemmas
Bibliography
Index.