Advanced Course in Numerical Algorithms with Python/SciPy
Avancerad kurs i numeriska algoritmer med Python/SciPy

FMNN25, 7.5 credits, A (Second Cycle)

Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED F/Pi
Date of Decision: 2024-04-15
Effective: 2024-05-08

General Information

Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective for: D4, E4-pv, F4, F4-bs, MMSR2, MSOC2, Pi4-bs
Language of instruction: The course will be given in English

Aim

The course is intended as an algorithm oriented complement to most of the basic and specialized courses in numerical analysis, which are focused on analysis of methods. The course emphasizes the coupling between complex numerical algorithms and modern programming languages.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

• be able to account for the basic principles of computational algorithms.
• be able to describe a number of important computational problems, and ways to attack them, in detail.

Competences and skills
For a passing grade the student must

• be able to write computational programmes at a high level.
• be able to code, test and evaluate the results of complex numerical algorithms, using established programme libraries.
• be able to carry out a group programming project, including identifying subproblems, distribution of tasks within the group and responsibility for the completion of his/her task.
• be able to describe a computational project through an oral presentation of their own code.

Judgement and approach
For a passing grade the student must

• be able to assess the performance of complex numerical algorithms.
• be able to argue for the importance of developing programs in a modular and flexible way.
• be able to critically analyse other students' solutions and presentations and evaluate alternative solutions in relation to their own solutions.

Contents

Introduction to Python for students already familiar with other programming languages.

The use of object oriented programming in scientific computing. Scipy/Numpy data structures.

Examples of complex numerical algorithms from various subjects in numerical analysis.

Automatic tests in scientific computing. Graphical representation of mathematical results (animation). The use of Python to control system processes.

The course may be complemented with special contributions of invited guest teachers.

Examination details

Grading scale: UG - (U, G) - (Fail, Pass)
Assessment:

Three major programming projects to be carried out in groups, and to be presented at a seminar. Opposition on the report of another group. Attendance at all presentations is compulsory.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Modules
Code: 0111. Name: Advanced Course in Numerical Algorithms with Python/SciPy.
Credits: 7.5. Grading scale: UG - (U, G).

Admission

Assumed prior knowledge: Basic course in numerical analysis. Programming experience in some of the languages Java, C, C++, Fortran, Python and Matlab.
The number of participants is limited to: 60
Selection: Completed university credits within the programme according to Ladok. (For students in master's programmes 180 credits corresponding to a bachelor degree are added.) Priority is given to students enrolled on programmes that include the course in their curriculum.
Kursen överlappar följande kurser: NUMN25 NUMN21

Reading list

• Führer, C, Solem, J.E., Verdier, O:: Scientific Computing with Python 3 - Second Edition. Packt Publishing, 2021, ISBN: 9781838822323.

Contact

Course coordinator: Andreas Langer, Andreas.Langer@math.lth.se
Director of studies: Anders Holst, studierektor@math.lth.se
Course administrator: Student Office, expedition@math.lth.se
Course homepage: https://canvas.education.lu.se/courses/20391