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.
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.
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.
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.
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