Table of Contents

• Rules
• Preface
• Prologue
• Mathematical Symbols
• Chapter 1: Introduction
• Chapter 2: A Framework for Evaluating Functions
• Chapter 3: Fundamentals of Forward and Reverse
• Chapter 4: Memory Issues and Complexity Bounds
• Chapter 5: Repeating and Extending Reverse
• Chapter 6: Implementation and Software
• Chapter 7: Sparse Forward and Reverse
• Chapter 8: Exploiting Sparsity by Compression
• Chapter 9: Going beyond Forward and Reverse
• Chapter 10: Jacobian and Hessian Accumulation
• Chapter 11: Observations on Efficiency
• Chapter 12: Reversal Schedules and Checkpointing
• Chapter 13: Taylor and Tensor Coefficients
• Chapter 14: Differentiation without Differentiability
• Chapter 15: Implicit and Iterative Differentiation
• Epilogue
• List of Figures
• List of Tables
• Assumptions and Definitions
• Propositions, Corollaries, and Lemmas
• Bibliography
• Index

Promotional Information

A comprehensive treatment of algorithmic, or automatic, differentiation.

About the Author

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.

Andrea Walther studied mathematics and economy at the University of Bayreuth. She holds a doctorate degree from the Technische Universität Dresden. Since 2003 Andrea Walther has been Juniorprofessor for the analysis and optimization of computer models at the Technische Universität Dresden. Her main research interests are scientific computing and nonlinear optimization.