
Structure-Preserving Doubling Algorithms for Nonlinear Matrix Equations
$180.22
- Paperback
144 pages
- Release Date
1 September 2018
Summary
Nonlinear matrix equations arise frequently in applied science and engineering. This is the first book to provide a unified treatment of structure-preserving doubling algorithms, which have been recently studied and proven effective for notoriously challenging problems, such as fluid queue theory and vibration analysis for high-speed trains. The authors present recent developments and results for the theory of doubling algorithms for nonlinear matrix equations associated with regular matrix p…
Book Details
| ISBN-13: | 9781611975352 |
|---|---|
| ISBN-10: | 1611975352 |
| Author: | Tsung-Ming Huang, Ren-Cang Li, Wen-Wei Lin |
| Publisher: | Society for Industrial & Applied Mathematics,U.S. |
| Imprint: | Society for Industrial & Applied Mathematics,U.S. |
| Format: | Paperback |
| Number of Pages: | 144 |
| Release Date: | 1 September 2018 |
| Weight: | 353g |
| Dimensions: | 229mm x 152mm |
| Series: | Fundamentals of Algorithms |
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About The Author
Tsung-Ming Huang
Tsung-Ming Huang is a professor in the department of mathematics at National Taiwan Normal University in Taipei, Taiwan. His research interests include large sparse linear systems, eigenvalue problems, and matrix equations.Ren-Cang Li is a professor in the department of mathematics at University of Texas at Arlington. His research interests include floating-point support for scientific computing, large and sparse linear systems, eigenvalue problems, and model reduction, machine learning, and unconventional schemes for differential equations.Wen-Wei Lin is a life-time chair professor in the department of applied mathematics at National Chiao Tung University in Taiwan. His research interests include numerical analysis, matrix computation in linear systems, eigenvalue problems, optimal controls, large-scale optimization in data science, chaotic dynamical systems, and computational conformal geometry with applications.
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