Domain Decomposition Methods in Science and Engineering XIX [electronic resource] / edited by Yunqing Huang, Ralf Kornhuber, Olof Widlund, Jinchao Xu.
Tipo de material:
TextoSeries Lecture Notes in Computational Science and Engineering, 78Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XXIV, 472 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642113048Trabajos contenidos: SpringerLink (Online service)Tema(s): Mathematics | Computer science | Computer science -- Mathematics | Mathematics | Computational Mathematics and Numerical Analysis | Computational Science and Engineering | Numerical and Computational Physics | Mathematics of ComputingFormatos físicos adicionales: Sin títuloClasificación CDD: 518 | 518 Clasificación LoC:QA71-90Recursos en línea: de clik aquí para ver el libro electrónico Springer eBooksResumen: 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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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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