Numerical Analysis for Scientists and Engineers develops the subject gradually by illustrating several examples for both the beginners and the advanced readers using very simple language. The classical and recently developed numerical methods are derived from mathematical and computational points of
Numerical Analysis for Engineers and Scientists
โ Scribed by G. Miller
- Publisher
- Cambridge University Press
- Year
- 2014
- Tongue
- English
- Leaves
- 583
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Striking a balance between theory and practice, this graduate-level text is perfect for students in the applied sciences. The author provides a clear introduction to the classical methods, how they work and why they sometimes fail. Crucially, he also demonstrates how these simple and classical techniques can be combined to address difficult problems. Many worked examples and sample programs are provided to help the reader make practical use of the subject material. Further mathematical background, if required, is summarized in an appendix. Topics covered include classical methods for linear systems, eigenvalues, interpolation and integration, ODEs and data fitting, and also more modern ideas like adaptivity and stochastic differential equations.
โฆ Subjects
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๐ SIMILAR VOLUMES
<p><span>This textbook provides a compact but comprehensive treatment that guides students through applied numerical analysis, using MATLABยฎ/Simulinkยฎ.ย Ideal as a hands-on source for courses in Numerical Analysis, this text focuses on solving problems using market-standard software, corresponding t
Topics are divided between review material on the mathematics background; numerical-analysis methods such as differentiation, integration, the solution of differential equations from engineering, life and physical sciences; data-analysis applications including least-squares fitting, splines and Four
Topics are divided between review material on the mathematics background; numerical-analysis methods such as differentiation, integration, the solution of differential equations from engineering, life and physical sciences; data-analysis applications including least-squares fitting, splines and Four
Topics are divided between review material on the mathematics background; numerical-analysis methods such as differentiation, integration, the solution of differential equations from engineering, life and physical sciences; data-analysis applications including least-squares fitting, splines and Four