Credits: 5

Schedule: 28.10.2019 - 09.12.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

II Autumn (2018-2019, 2019-2020)


Learning Outcomes (valid 01.08.2018-31.07.2020): 

Students learn to analyze and solve problems in linear algebra that occur often in scientific computing, data fitting and optimization. The main focus is on solution of linear systems, least squares problems and eigenvalue problems. After the course, the students can choose the best solution method for each problem and have a good understanding on issues related to numerical stability of the applied algorithms. 


Content (valid 01.08.2018-31.07.2020): 

Matrix decompositions and their numerical computation, eigenvalue iterations, sparse matrices, iterative solution of linear systems.


Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Teaching methods: lectures, exercises and exam

Assessment methods: exercises and an exam


Workload (valid 01.08.2018-31.07.2020): 

contact hours 36h (no compulsory attendance) 

self-study ca 100h


Study Material (valid 01.08.2018-31.07.2020): 

All essential material is included in the lecture notes that are available at the course's homepage.


Substitutes for Courses (valid 01.08.2018-31.07.2020): 



Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

MS-A00XX, MS-A01XX, MS-A02XX, MS-C134X. The courses MS-A03XX and MS-C1540 may also be useful.


Grading Scale (valid 01.08.2018-31.07.2020): 



Registration for Courses (valid 01.08.2018-31.07.2020): 




Registration and further information