Numerical Methods: Using MATLAB
Autor George Lindfield, John Pennyen Limba Engleză Paperback – 27 noi 2025
By using MATLAB it is possible for the readers to tackle some large and difficult problems and deepen and consolidate their understanding of problem solving using numerical methods. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization and many other fields. The text will be a valuable aid to people working in a wide range of fields, such as engineering, science and economics.
- Features many numerical algorithms, their fundamental principles, and applications
- Provides a user-friendly resource that is written in a conversational and approachable style
- Contains over 60 algorithms implemented as MATLAB® functions and over 100 MATLAB® scripts applying numerical algorithms to specific examples
- Includes a solutions manual and image bank for instructors and downloadable versions of all MATLAB file scripts and functions listed in the text
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Specificații
ISBN-13: 9780443264986
ISBN-10: 0443264988
Pagini: 686
Dimensiuni: 191 x 235 mm
Greutate: 0.45 kg
Ediția:5th edition
Editura: ELSEVIER SCIENCE
ISBN-10: 0443264988
Pagini: 686
Dimensiuni: 191 x 235 mm
Greutate: 0.45 kg
Ediția:5th edition
Editura: ELSEVIER SCIENCE
Cuprins
1. An Introduction to Matlab
2. Linear Equations and Eigensystems
3. Solution of Nonlinear Equations
4. Differentiation and Integration
5. Ordinary Differential Equations
6. Partial Differential Equations
7. Interpolation and Least Squares Approximation
8. Analyzing Data using Discrete Transforms
9. Optimization Methods
10. Machine Learning
11. Applications of the Symbolic Toolbox
2. Linear Equations and Eigensystems
3. Solution of Nonlinear Equations
4. Differentiation and Integration
5. Ordinary Differential Equations
6. Partial Differential Equations
7. Interpolation and Least Squares Approximation
8. Analyzing Data using Discrete Transforms
9. Optimization Methods
10. Machine Learning
11. Applications of the Symbolic Toolbox