Numerical Methods with MATLAB provides a highly-practical reference work to assist anyone working with numerical methods. A wide range of techniques are introduced, their merits discussed and fully working MATLAB code samples supplied to demonstrate how they can be coded and applied.
Numerical methods have wide applicability across many scientific, mathematical, and engineering disciplines and are most often employed in situations where working out an exact answer to the problem by another method is impractical.
Numerical Methods with MATLAB presents each topic in a concise and readable format to help you learn fast and effectively. It is not intended to be a reference work to the conceptual theory that underpins the numerical methods themselves. A wide range of reference works are readily available to supply this information. If, however, you want assistance in applying numerical methods then this is the book for you.
What you’ll learn
Underlying concepts and methodology behind numerical methods and simulations
The types of numerical methods that are available
Basic numerical operations and techniques and their applications in numerical methods
How to apply a wide range of numerical techniques and simulations (including Monte Carlo simulations) within MATLAB and visualize the solution
Clear examples of how various optimization techniques including evolutionary algorithms can be employed to solve common problems
How to perform numerical regression and model fitting by implementing your own programs that go beyond those available in the MATLAB toolbox.
Who this book is for
This book is ideal for professionals, undergraduates, and postgraduates who need to apply numerical methods to solving day-to-day problems within the MATLAB environment. While basic familiarity with both numerical methods and MATLAB is assumed, the book's practical approach makes it very accessible to a wide range of readers.
Table of Contents
1. Introduction to MATLAB
2. Matrix Representation, Operations and Vectorization
3. Numerical Techniques
5. Introduction to Simulation
6. Monte Carlo Simulations
8. Evolutionary Algorithms
9. Regression and Model Fitting
10. Differential Equations and System Dynamics