Numerical Programming 1 (CSE)
Module MA3305
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 WS 2011/2
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 |
Basic Information
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
Content
Fundamentals of analysis and linear algebra;
Condition numbers, floating point arithmetic, stability;
Solving linear systems (Gaussian elimination, least squares);
Eigenvalue problems;
Interpolation (algebraic and trigonometric polynomials, splines);
Intgration (sum rules, Gaussian quadrature);
Iterative methods (Jacobi, Gauß-Seidel, conjugate gradient method (CG), Newton);
Runge-Kutta method.
Condition numbers, floating point arithmetic, stability;
Solving linear systems (Gaussian elimination, least squares);
Eigenvalue problems;
Interpolation (algebraic and trigonometric polynomials, splines);
Intgration (sum rules, Gaussian quadrature);
Iterative methods (Jacobi, Gauß-Seidel, conjugate gradient method (CG), Newton);
Runge-Kutta method.
Learning Outcome
At the end of the module, the students are able to understand the mathematical principles of basic numerical algorithms for solving linear systems and eigenvalue problems, for interpolation and integration.
Preconditions
working knowledge of analysis and linear algebra
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
WS 2020/1
WS 2019/20
WS 2018/9
WS 2017/8
WS 2016/7
WS 2015/6
WS 2014/5
WS 2013/4
WS 2012/3
WS 2011/2
WS 2010/1
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 4 | Numerical Programming 1 CSE | Lasser, C. |
Mon, 12:00–14:00, MI HS2 Wed, 12:00–14:00, MI HS3 |
documents |
| UE | 2 | Numerical Programming 1 CSE (Exercise Session) | Lasser, C. Schreiber, A. |
Thu, 11:45–13:30, MW 0608m |
Learning and Teaching Methods
This module comprises lectures and accompanying tutorials. Students will be encouraged to study the literature and to get involved with the topics in depth. In the tutorials, concrete numerical problems will be solved and selected examples will be discussed.
Media
blackboard, LCD projector, assignments
Literature
Quarteroni /Saleri /Gervasio: Scientific Computing with MATLAB and Octave, Springer 2010.
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.
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.
Module Exam
Description of exams and course work
The course examination will happen as three online graded homework assignments (G1, G2, G3), managed via the TUMexam system. The final grade is the next smallest grade to the average (G1+G2+G3)/3 according to the TUM grading system (x.0, x.3, x.7).
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.
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.
Exam Repetition
The exam may be repeated at the end of the semester.