Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Scientific Computation (SCIENTCOMP)
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Table of contents (21 chapters)
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Front Matter
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Aspects of Conjugate Gradient Algorithms
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Front Matter
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Applications to the Solution of Linear and Nonlinear Partial Differential Equations
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Front Matter
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Keywords
About this book
The position taken in this collection of pedagogically written essays is that conjugate gradient algorithms and finite element methods complement each other extremely well.
Via their combinations practitioners have been able to solve complicated, direct and inverse, multidemensional problems modeled by ordinary or partial differential equations and inequalities, not necessarily linear, optimal control and optimal design being part of these problems.
The aim of this book is to present both methods in the context of complicated problems modeled by linear and nonlinear partial differential equations, to provide an in-depth discussion on their implementation aspects. The authors show that conjugate gradient methods and finite element methods apply to the solution of real-life problems. They address graduate students as well as experts in scientific computing.
Editors and Affiliations
Bibliographic Information
Book Title: Conjugate Gradient Algorithms and Finite Element Methods
Editors: Michal Křížek, Pekka Neittaanmäki, Sergey Korotov, Roland Glowinski
Series Title: Scientific Computation
DOI: https://doi.org/10.1007/978-3-642-18560-1
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2004
Hardcover ISBN: 978-3-540-21319-2Published: 11 June 2004
Softcover ISBN: 978-3-642-62159-8Published: 01 November 2012
eBook ISBN: 978-3-642-18560-1Published: 06 December 2012
Series ISSN: 1434-8322
Series E-ISSN: 2198-2589
Edition Number: 1
Number of Pages: XV, 384
Topics: Numerical and Computational Physics, Simulation, Computational Science and Engineering, Fluid- and Aerodynamics, Computational Intelligence