2023-2024 / MATH0500-1

Introduction to numerical algorithmic


24h Th, 14h Pr, 6h Labo., 45h Proj.

Number of credits

 Bachelor of Science (BSc) in Computer Science5 crédits 
 Master of Science (MSc) in Computer Science5 crédits 


Quentin Louveaux

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Numerical analysis is at the boundary between Mathematics and Computer Science. It consists in studying how to practically obtain in a computer different mathematical concepts studied in other courses.

This course gives a brief introduction on good ways to implement a numerical method.

The following topics are considered:
- number representations in a computer
- Non-linear equations and systems

- interpolation
- Linear algebra
- Sparse linear algebra
- Monte-Carlo methods

Learning outcomes of the learning unit

- representation of numbers in a computer and implications on the roundoff errors in floating point computing
- interpoolation issues - numerical methods for the resolution of nonlinear equations - basics of numerical linear algebra - basics of sparse linear algebra

Prerequisite knowledge and skills

A basic course in linear algebra

Planned learning activities and teaching methods

Five tutorials are organized.
A programming project in C (by groups of 2) must be carried out during the semester.

The project must be presented during the first session and cannot be presented in second sesssion.

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Additional information:


Recommended or required readings

Lecture notes are available on ecampus.

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )

Written work / report

Additional information:

A written exam is organized and counts for 2/3 of the final grade.

The implementation project counts for 1/3 of the final grade. It must be submitted in the first session. The project cannot be resubmitted in second session.

If it is to the student's advantage, the final grade can be 100% of the exam grade.

Work placement(s)

Organisational remarks and main changes to the course

All documents are available through a OneDrive repository.



Association of one or more MOOCs