Numerical Methods Using MATLAB�, Fifth Edition continues to provide a clear and rigorous introduction to a wide range of numerical methods that have practical applications. The authors’ approach is to integrate MATLAB with numerical analysis in a way which adds clarity to the numerical analysis and develops familiarity with MATLAB. MATLAB graphics and numerical output are used extensively to clarify complex problems and give a deeper understanding of their nature. The text provides an extensive reference providing numerous useful and important numerical algorithms that are implemented in MATLAB to help researchers analyze a particular outcome.
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.
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.
Table of Contents
1. An Introduction to Matlab2. 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
