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Book Categories |
| 1 | Introduction - the nature of high-performance computation | 3 |
| 2 | Theoretical considerations - complexity | 27 |
| 3 | Machine implementations | 44 |
| 4 | Building blocks - floating point numbers and basic linear algebra | 103 |
| 5 | Direct methods for linear systems and LU decomposition | 126 |
| 6 | Direct methods for systems with special structure | 162 |
| 7 | Error analysis and QR decomposition | 172 |
| 8 | Iterative methods for linear systems | 186 |
| 9 | Finding eigenvalues and eigenvectors | 206 |
| 10 | Monte Carlo simulation | 233 |
| 11 | Monte Carlo optimization | 244 |
| Price | Condition | Delivery | Seller | Action |
| $110.00 | Digital |
| WonderClub |
Sue Branscomb
reviewed Parallel and Vector Scientific Computing on August 30, 2014
Parallel and Vector Scientific Computing
If you read this book as first time in order to understand Computational Complexity, then you're probably not going to like it. You need to read Sipser's textbook before you read this book and make sure that you have a strong background in discrete mathematics (if not, then see Rosen's textbook in Discrete Mathematics). This book is recommended by best people in complexity and algorithms such as Scott Aaronson, Sipser, etc. You need to solve as many exercises as you go.
The book has five parts:
Part I: Algorithms
Part II: Logic
Part III: P and NP
Part IV: Inside P
Part V: Beyond NP
The most important parts are in Part I, III.
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