[PDF.41zj] Spectral Methods: Algorithms, Analysis and Applications (Springer Series in Computational Mathematics)
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Spectral Methods: Algorithms, Analysis and Applications (Springer Series in Computational Mathematics)
[PDF.av97] Spectral Methods: Algorithms, Analysis and Applications (Springer Series in Computational Mathematics)
Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang epub Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang pdf download Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang pdf file Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang audiobook Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang book review Spectral Methods: Algorithms, Analysis Jie Shen, Tao Tang, Li-Lian Wang summary
| #7330913 in Books | 2013-11-27 | 2013-11-27 | Original language:English | PDF # 1 | 9.25 x1.10 x6.10l,.0 | File type: PDF | 472 pages||0 of 4 people found the following review helpful.| Fast, perfect|By YUNHUA XUE|Perfect. Received it before the estimated time. The book looks new, just a ex library print in the first page. Satisfied with this purchase.|||From the reviews:|“This is a largely self-contained book on major parts of the application of spectral methods to the numerical solution of partial differential equations … . The material is accessible to … advanced students of mathematics
Along with finite differences and finite elements, spectral methods are one of the three main methodologies for solving partial differential equations on computers. This book provides a detailed presentation of basic spectral algorithms, as well as a systematical presentation of basic convergence theory and error analysis for spectral methods. Readers of this book will be exposed to a unified framework for designing and analyzing spectral algorithms for a variety of prob...
You easily download any file type for your device.Spectral Methods: Algorithms, Analysis and Applications (Springer Series in Computational Mathematics) | Jie Shen, Tao Tang, Li-Lian Wang.Not only was the story interesting, engaging and relatable, it also teaches lessons.