Domain Decomposition Methods in Science and Engineering XIX [electronic resource] / edited by Yunqing Huang, Ralf Kornhuber, Olof Widlund, Jinchao Xu.
By: Huang, Yunqing [editor.].
Contributor(s): Kornhuber, Ralf [editor.] | Widlund, Olof [editor.] | Xu, Jinchao [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computational Science and Engineering: 78Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XXIV, 472 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642113048.Subject(s): Mathematics | Computer science | Computer science -- Mathematics | Mathematics | Computational Mathematics and Numerical Analysis | Computational Science and Engineering | Numerical and Computational Physics | Mathematics of ComputingDDC classification: 518 | 518 Online resources: Click here to access online
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Springer eBooksSummary: These are the proceedings of the 19th international conference on domain decomposition methods in science and engineering. Domain decomposition methods are iterative methods for solving the often very large linear or nonlinear systems of algebraic equations that arise in various problems in mathematics, computational science, engineering and industry. They are designed for massively parallel computers and take the memory hierarchy of such systems into account. This is essential for approaching peak floating point performance. There is an increasingly well-developed theory which is having a direct impact on the development and improvement of these algorithms.
These are the proceedings of the 19th international conference on domain decomposition methods in science and engineering. Domain decomposition methods are iterative methods for solving the often very large linear or nonlinear systems of algebraic equations that arise in various problems in mathematics, computational science, engineering and industry. They are designed for massively parallel computers and take the memory hierarchy of such systems into account. This is essential for approaching peak floating point performance. There is an increasingly well-developed theory which is having a direct impact on the development and improvement of these algorithms.
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