Description
Book SynopsisThis textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science.
Features:
- No previous experience in Python is required.
- Includes simplified computer code for fast-paced learning and transferable skills development.
- Includes practical problems ideal for project assignments and distance learning.
- Presents both intuitive and rigorous faces of modern scientific computing.
- Provides an introduction to neural networks and machine learning.
Table of Contents1. Introduction to Python. 2. Matrices and Python. 3. Scientific computing. 4. Calculus facts. 5. Roots of equations. 6. Interpolation and approximation. 7. Numerical integration. 8. Numerical differentiation and applications to differential equations. 9. Numerical linear algebra. 10. Best approximations. 11. Unconstrained optimization and neural networks. 12. Eigenvalue problems.
