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

FMNN25F, 7.5 credits

Valid from: Autumn 2014
Decided by: FN1/Anders Gustafsson
Date of establishment: 2015-01-14

General Information

Division: Numerical Analysis
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course codes: FMNN25, NUMN25
Teaching language: English

Aim

To give doctoral students with experience of other programming languages training in implementing advanced numeric algorithms in Python/Scipy, in order to use this in other courses, in research or in industry.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• have developed an understanding for the basic principles of computational algorithms.
• have improved her/his knowledge about a number of important computational problems, and ways to attack them.

Competences and Skills

For a passing grade the doctoral student must

• have developed a programming ability (for computational purposes) at a high level.
• have learnt how 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 account for a computational project, both in a written report and orally.

Course Contents

Introduction to Python for students already familar with another programming language. The use of object oriented programming in scientific computing, Scipy/Numpy datastructures. Examples of complex numerical algorithms from varying subjects in numerical analysis, Linking to advanced numerical libraries in C and Fortran (Netlib). Automatic tests in scientific computing. Graphical representation of mathematical results (animation). The use of Python to control system processes.

Course Literature

Führer, C., Solem, J. & Verdier, O.: Computing with Python: An Introduction to Python for Science and Engineering. Pearson, 2013. ISBN 9780273786436.

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written assignments, miscellaneous. A larger programming project to be carried out in group, with a written report to be presented at a seminar. Opposition on the report of another group.
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Basic course in numerical analysis. Programming experience in some of the languages Java, C, C++, Fortran, Python and Matlab.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: http://ctr.maths.lu.se/na/courses/FMNN25/