Numerical Analysis and Scientific Computation
Series: Textbooks in Mathematics;
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Product details:
- Edition number 2
- Publisher Chapman and Hall
- Date of Publication 26 August 2024
- ISBN 9781032204833
- Binding Paperback
- No. of pages582 pages
- Size 254x178 mm
- Weight 453 g
- Language English
- Illustrations 29 Illustrations, black & white; 29 Line drawings, black & white; 2 Tables, black & white 524
Categories
Short description:
This is an introductory single-term numerical analysis text with a modern scientific computing flavor. It offers an immediate immersion in numerical methods featuring an up-to-date approach to computational matrix algebra and methods used in actual software packages, highlighting how hardware concerns can impact the choice of algorithm.
MoreLong description:
This is an introductory single-term numerical analysis text with a modern scientific computing flavor. It offers an immediate immersion in numerical methods featuring an up-to-date approach to computational matrix algebra and an emphasis on methods used in actual software packages, always highlighting how hardware concerns can impact the choice of algorithm. It fills the need for a text that is mathematical enough for a numerical analysis course yet applied enough for students of science and engineering taking it with practical need in mind.
The standard methods of numerical analysis are rigorously derived with results stated carefully and many proven. But while this is the focus, topics such as parallel implementations, the Basic Linear Algebra Subroutines, halfto quadruple-precision computing, and other practical matters are frequently discussed as well.
Prior computing experience is not assumed. Optional MATLAB subsections for each section provide a comprehensive self-taught tutorial and also allow students to engage in numerical experiments with the methods they have just read about. The text may also be used with other computing environments.
This new edition offers a complete and thorough update. Parallel approaches, emerging hardware capabilities, computational modeling, and data science are given greater weight.
Table of Contents:
1. Nonlinear Equations
2. Linear Systems
4. Polynomial Interpolation
5. Numerical Integration
6. Differential Equations
7. Nonlinear Optimization
