| Edition |
2nd ed. / Andrea Walther. |
| Physical description |
1 electronic text (xxi, 438 p.) : ill., digital file. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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. |
| Restrictions |
Restricted to subscribers or individual electronic text purchasers. |
| Summary |
This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. |
| Other formats |
Also available in print version. |
| System notes |
Mode of access: World Wide Web. |
|
System requirements: Adobe Acrobat Reader. |
| Notes |
Description based on title page of print version. |
| Other author |
Walther, Andrea, 1970-
|
|
Society for Industrial and Applied Mathematics.
|
| Subject |
Differential calculus -- Data processing.
|
|
Algorithmic Differentiation |
|
Computation of Derivatives |
|
Chain rule |
|
Computational graph |
|
Adjoints |
| Variant Title |
Principles and techniques of algorithmic differentiation. |
| ISBN |
9780898717761 (electronic bk.) |
|
9780898716597 (alk. paper) |
| Standard Number |
OT105 |
|
