Numerical Programming 1 (CSE)
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2012
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2021/2||WS 2020/1||SS 2020||SS 2012||WS 2011/2|
MA3305 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Condition numbers, floating point arithmetic, stability;
Solving linear systems (Gaussian elimination, least squares);
Interpolation (algebraic and trigonometric polynomials, splines);
Intgration (sum rules, Gaussian quadrature);
Iterative methods (Jacobi, Gauß-Seidel, conjugate gradient method (CG), Newton);
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||4||Numerical Programming 1 CSE||Bornemann, F.||
Mon, 12:00–14:00, MI HS2
Wed, 12:00–14:00, MI HS3
|UE||2||Numerical Programming 1 CSE (Exercise Session)||Bornemann, F. Ludwig, C.||
Thu, 11:45–13:30, MW 1237
and dates in groups
Learning and Teaching Methods
Moler: Numerical Computing with MATLAB, SIAM, 2004.
Press, Flannery, Teukolsky, Vetterling: Numerical Recipes.Cambridge University Press, http://www.nr.com/.
Strang: Introduction to Linear Algebra, Wellesley-Cambridge, 2009.
Strang, Calculus, Wellesley-Cambridge, 1991.
Description of exams and course work
Students demonstrate that they have gained deeper knowledge of the mathematical concepts of the numerical algorithms presented in the course. The students are expected to be able to derive the methods, to explain their properties, to read and write pseudocode of the algorithms, and to apply them to specific examples.
The exam may be repeated at the end of the semester.