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Evaluating Derivatives: Principles And Techniques Of Algorithmic Differentiation (Frontiers In Applied Mathematics, Series Numbe,Used
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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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- Q: What is the page count of this book? A: This book has three hundred and ninety pages. It provides a comprehensive overview of algorithmic differentiation techniques.
- Q: What are the dimensions of this book? A: The book measures six point seventy-five inches in length, zero point seventy-five inches in width, and nine point five inches in height.
- Q: What type of binding does this book have? A: This book is available in paperback binding. Paperback editions are typically lightweight and flexible.
- Q: How can I apply the concepts from this book? A: You can apply the concepts by using automatic differentiation for functions defined in computer programs. This is useful in scientific computations and optimization tasks.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for readers with a basic understanding of calculus and programming. It covers principles that can be accessed by both novices and experienced readers.
- Q: Can I use this book for advanced studies? A: Yes, this book is suitable for advanced studies in algorithmic differentiation. It includes techniques that are applicable to complex scientific computations.
- Q: How should I store this book? A: Store this book in a cool, dry place away from direct sunlight. This helps preserve the binding and pages over time.
- Q: How can I keep this book in good condition? A: To keep this book in good condition, avoid bending the cover and pages. Additionally, use bookmarks instead of folding pages.
- Q: Does this book contain any adult content? A: No, this book does not contain adult content. It is focused on mathematical principles and techniques.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, you should contact the seller or retailer for a return or exchange. They typically have policies in place for such issues.
- Q: Is there a warranty on this book? A: No, there is typically no warranty on books. However, you can check with the retailer for their specific return policy.
- Q: What if I need help understanding the book's material? A: If you need help understanding the material, consider looking for online resources or study groups focused on algorithmic differentiation.
- Q: Are there any recommended prerequisites for reading this book? A: Yes, it is recommended to have a basic understanding of calculus and programming concepts before reading this book.
- Q: Can I find similar books in this category? A: Yes, there are many books in the calculus and applied mathematics category that cover similar topics. Look for titles on algorithmic differentiation and optimization.
- Q: What is the author's background? A: The author of this book is Andreas Griewank, who is recognized for his contributions to the field of algorithmic differentiation and applied mathematics.