Numerical analysis for engineers and scientists / G. Miller, Department of Chemical Engineering and Materials Science, University of California, Davis.
By: Miller, G [author.]
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Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2014Description: 1 online resource (x, 572 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781139108188 (ebook).Other title: Numerical Analysis for Engineers & Scientists.Subject(s): Numerical analysisAdditional physical formats: Print version: : No titleDDC classification: 518 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
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.
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