02023 - Numerical Computing

Learning outcomes

At the end of the course students learn the basics of Numerical Computation as error analysis, data interpolation, numerical integration, non-linear equations, linear systems. They are able to solve problems of scientific computing.

Course contents

1. Finite numbers and errors. Conditioning and stability.

2. Numerical methods for the following topics in numerical calculus:

• Numerical linear algebra: matrix factorizations, solution of linear systems, linear least squares.
• Data approximation: polynomial interpolation, least squares approximation.
• Numerical integration.
• Optimization: iterative methods for the solution of scalar linear equations, numerical methods for multivariate optimization.
  

3. Applications with Matlab

• Introduction to Python scientific libraries  

 

• Laboratory exercises in Matlab on the previous topics.

Teaching methods

Lectures and Laboratory exercises.

The class attendance is highly recommended for the learning and for the exam preparation.

Assessment methods

It is mandatory to complete the assigned homework and/or project to have the exam.

 

The exam is  a test in the Laboratory, constituted by written questions and exercises and a program on the computer. A brief discussion on the homeworks is possible.

The final score is the sum of:

• the score of the exam (max 24/30)
• the score of the homeworks/projects (max(8/30)

If the final score is greater than 30, the laude is assigned.

Teaching tools

Slides and programs from the teacher.

Office hours

See the website of Elena Loli Piccolomini