Module 39-Inf-AOpt Applied Optimisation

Faculty

Person responsible for module

Regular cycle (beginning)

Every winter semester

Credit points and duration

5 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Non-official translation of the module descriptions. Only the German version is legally binding.

Students learn in the lectures and the exercise courses to be able to phrase a given problem as optimization problem and to identify its properties. Depending on the latter, students are able to select a suitable problem solver and they know the properties of found solutions. Students are able to use popular toolboxes. The module includes an exam at the end of the term.

Content of teaching

The goal is to cover important models to phrase optimization problems and important algorithmic approaches to solve those, including constraint versus unconstraint optimization, linear and convex optimization, duality, nonlinear optimization, discrete optimization and relaxation. A few important methods are covered including conjugate gradient, quasi Newton methods such as LBFGS, interior point methods, Lagrange multipliers and barrier functions and exemplary global methods such CMA-ES.

Recommended previous knowledge

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Necessary requirements

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Explanation regarding the elements of the module

Module structure: 1 bPr 1

Courses

Applied Optimisation
Type exercise
Regular cycle WiSe
Workload5 60 h (30 + 30)
LP 2
Applied Optimisation
Type lecture
Regular cycle WiSe
Workload5 60 h (30 + 30)
LP 2 [Pr]

Examinations

portfolio with final examination
Allocated examiner Teaching staff of the course Applied Optimisation (lecture)
Weighting 1
Workload 30h
LP2 1

Portfolio consisting of per default weekly exercises or programming tasks and final written exam (per default 60 minutes) or final oral exam (per default 15 minutes). The exercises are based on the content of the lecture and enable students to train and further investigate the topics. It is required that a sufficient percentage of the exercises are successfully completed (per default 50% of the total number of points which can be achieved during a semester). The final oral exam concerns both, the content of the lecture as well as the exercises.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 1 1. one semester Obli­gation
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 2 1. one semester Obli­gation

Automatic check for completeness

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Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
Study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.

Sidebar

Elements of the module

Courses

Examinations

Programme of lectures (eKVV)

Programme of lectures (eKVV)

Show lists of modules

Data Science / Master of Science // Variante 1

Data Science / Master of Science // Variante 2