Description

Theory
1

Laboratory
3

Instructors

Ana Júlia Viamonte

Contents

1. Introduction to Python (1.5 weeks)
2. Approximations and errors (1 week)
3. Numerical solution of algebraic equations (1.5 weeks)
4. Numerical solution of systems (2 weeks)
5. Polynomial interpolation (1 week)
6. Numerical Differentiation and Integration (2 weeks)
7. Numerical solution of Differential equations (2 weeks)

Learning Outcomes

It is intended to provide students with an intuitive and practical knowledge of some numerical methods and that they learn to use the knowledge of Mathematical Analysis and Linear Algebra, previously acquired, to study approximate models of representation of physical phenomena and to solve approximately way the associated numerical problems. At the end of the semester, students must be able to:
OB1: Study errors and their propagation.
OB2: Use iterative methods to solve nonlinear equations and systems of linear and nonlinear equations;
OB3: To approach problems through Polynomial interpolation.
OB4: Use numerical methods to solve integrals.
OB5: Use numerical methods to solve differential equations.

Particular importance will be given to the process of developing algorithms and to the Python implementation of the approximate formulas obtained for several exact models studied.