- eBook:Practical Numerical and Scientific Computing with MATLAB® and Python
- Author:Eihab B. M. Bashier
- Edition:1 edition
- Data:March 11, 2020
- Pages:348 pages
- Format:PDF, ePUB
Description of Practical Numerical and Scientific Computing with MATLAB® and Python ebook
The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems.
This book has the following advantages:
- It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields.
- It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python.
- It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems.
- Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs.
1. Solving Linear Systems Using Direct Methods
2. Solving Linear Systems with Iterative and Least Squares Methods
3. Ill-Conditioning and Regularization Techniques in Solutions of Linear Systems
4. Solving a System of Nonlinear Equations
II - Data Interpolation and Solutions of Differential Equations
5. Data Interpolation
6. Numerical Differentiation and Integration
7. Solving Systems of Nonlinear Ordinary Differential Equations
8. Nonstandard Finite Difference Methods for Solving ODEs
III - Solving Linear, Nonlinear and Dynamic Optimization Problems
9. Solving Optimization Problems: Linear and Quadratic Programming
10. Solving Optimization Problems: Nonlinear Programming
11. Solving Optimal Control Problems
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