Title
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics, Series Numbe,New
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Product Description Algorithmic, or automatic, differentiation (AD) is concerned with the accurate and efficient evaluation of derivatives for functions defined by computer programs. No truncation errors are incurred, and the resulting numerical derivative values can be used for all scientific computations that are based on linear, quadratic, or even higher order approximations to nonlinear scalar or vector functions. In particular, AD has been applied to optimization, parameter identification, equation solving, the numerical integration of differential equations, and combinations thereof. Apart from quantifying sensitivities numerically, AD techniques can also provide structural information, e.g., sparsity pattern and generic rank of Jacobian matrices. Book Description Covers the fundamentals of AD and its software, methods for sparse problems, higher derivatives, nonsmooth problems, and program reversal schedules. From the Publisher This volume will be valuable for designers and users of algorithms and software for nonlinear computational problems. It opens up an exciting opportunity to develop new algorithms that reflect the availability of accurate derivatives and their true cost to achieve improvements in speed and reliability. Some familiarity with modern approaches to the seemingly straightforward task of evaluating derivatives will benefit any mathematician, scientist or engineer. About the Author Andreas Griewank is a former Senior Scientist of the Mathematics and Computer Science Division, Argonne National Laboratory. He is currently a Professor at the Institute of Scientific Computing in the Department of Mathematics at the Technical University Dresden.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation'? A: The book focuses on algorithmic differentiation (AD), which is a method for accurately and efficiently evaluating derivatives of functions defined by computer programs without incurring truncation errors.
- Q: Who is the author of this book? A: The author of 'Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation' is Andreas Griewank.
- Q: What are some applications of algorithmic differentiation discussed in the book? A: The book discusses various applications of algorithmic differentiation, including optimization, parameter identification, equation solving, and numerical integration of differential equations.
- Q: What is the condition of the used book available for purchase? A: The used book is in 'Good Condition', indicating that it may show some signs of use but remains functional and readable.
- Q: How many pages does this book have? A: The book contains 390 pages.
- Q: What is the binding type of this book? A: The book is available in paperback binding.
- Q: When was 'Evaluating Derivatives' published? A: The book was published on January 1, 1987.
- Q: What category does this book fall under? A: This book falls under the category of Calculus.
- Q: Does the book provide any structural information related to derivatives? A: Yes, the book discusses how AD techniques can provide structural information such as sparsity patterns and the generic rank of Jacobian matrices.
- Q: Is this book suitable for beginners in algorithmic differentiation? A: While the book covers advanced topics in algorithmic differentiation, it may be more suitable for readers with some background in calculus and numerical methods.