The Probability That a Numerical, Analysis Problem Is Difficult
Language: English
Published by Forgotten Books, 2018
- Softcover
- New
Seller: Forgotten Books, London, United KingdomForgotten Books
AbeBooks seller since June 2, 2021
Condition: New
US$ 17.29
Quantity: Over 20 available
Add to basketItem description from seller
Print on Demand. This book explores the likelihood that a numerical analysis problem will be difficult, the circumstances surrounding the circumstances that raise the difficulty, and statistical methods for understanding the frequency of these difficulties. Through this analysis, the author positions these problems within their broader historical context and presents a methodology to increase the probability that a problem will have a solution, which extends the applicability of numerical methods to new classes of problems. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.
Seller Inventory # 9781330257166_0
- Title
- The Probability That a Numerical, Analysis Problem Is Difficult
- Author
- James W. Demmel
- Publisher
- Forgotten Books
- Publication year
- 2018
- Condition
- New
- Book Type
- print-on-demand item
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1330257162
- ISBN 13
- 9781330257166
- Seller catalogs
- Statistics
Discover how likely numerical problems are to be hard—and why some problems stay solvable.
This book explains a probabilistic way to study numerical conditioning, showing how the distance to ill-posed problems affects accuracy and behavior in algorithms. It ties geometry, probability, and computer arithmetic into a practical view of when and why numerical methods work.
- Learn the idea that a problem’s condition number connects to how close it is to infinite sensitivity, and how this guides algorithmic reliability.
- See how uniform distributions and tubular neighborhoods help bound the chances of large errors in matrix inversion, polynomial zero finding, and eigenvalue computations.
- Understand how finite precision arithmetic changes the picture, and what this means for real-world computations and algorithm design.
- Explore the limits of the probabilistic model, including when discretization or clustering near ill-posed sets alters expected outcomes.
"Synopsis" may belong to another edition of this title.
Forgotten Books
London, United Kingdom
AbeBooks seller since June 2, 2021
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Forgotten Books’ Classic Reprint Series utilizes the latest technology to regenerate facsimiles of historically important writings. Careful attention has been made to accurately preserve the original format of each page whilst digitally enhancing the quality of the aged text.
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