Domain Decomposition Methods in Science and Engineering XX [electronic resource] / edited by Randolph Bank, Michael Holst, Olof Widlund, Jinchao Xu.

Contributor(s):

Series: Lecture Notes in Computational Science and Engineering ; 91Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XIX, 686 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783642352751

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

518 23

LOC classification:

QA71-90

Online resources:

Click here to access online

Contents:

Preface -- Part I: Plenary Presentations -- Part II: Minisymposia -- Part III: Contributed Presentations.

In: Springer eBooksSummary: These are the proceedings of the 20th international conference on domain decomposition methods in science and engineering. Domain decomposition methods are iterative methods for solving the often very large linearor nonlinear systems of algebraic equations that arise when various problems in continuum mechanics are discretized using finite elements. They are designed for massively parallel computers and take the memory hierarchy of such systems in mind. This is essential for approaching peak floating point performance. There is an increasingly well developed theory whichis having a direct impact on the development and improvements of these algorithms.

Item type:

eBooks

Average rating: 0.0 (0 votes)

Holdings ( 0 )
Title notes ( 2 )

No physical items for this record

Preface -- Part I: Plenary Presentations -- Part II: Minisymposia -- Part III: Contributed Presentations.

These are the proceedings of the 20th international conference on domain decomposition methods in science and engineering. Domain decomposition methods are iterative methods for solving the often very large linearor nonlinear systems of algebraic equations that arise when various problems in continuum mechanics are discretized using finite elements. They are designed for massively parallel computers and take the memory hierarchy of such systems in mind. This is essential for approaching peak floating point performance. There is an increasingly well developed theory whichis having a direct impact on the development and improvements of these algorithms.