LVs des Lehrstuhls für Efficient Algorithms
Masterpraktikum Novel Technologies in Graph Drawing and Network Visualization (CITHN7101, INHN4551)
| Vortragende/r (Mitwirkende/r) | |
|---|---|
| Nummer | 0000002575 |
| Art | Praktikum |
| Umfang | 6 SWS |
| Semester | Wintersemester 2025/26 |
| Unterrichtssprache | English |
| Stellung in Studienplänen | Siehe TUMonline |
Teilnahmekriterien
Beschreibung
This course focuses on implementing advanced algorithms to participate
in two prominent algorithmic challenges: the Graph Drawing Challenge and
the Parameterized Algorithms and Computational Experiments (PACE)
Challenge. The course aims to deepen students' understanding of graph
theory, algorithm design, and fixed-parameter tractability (FPT).
Students create a visualization of a given network of medium to large scale. The group is divided into several teams for this purpose. During the process, students acquire knowledge in the areas of network analysis, graph drawing and information visualization and practice applying this knowledge. Students also use suitable visualization software packages to create the visualization. The students present the current state of development at regular intervals and give each other feedback.
Throughout the course, students will develop and refine their
algorithms, receive feedback, and compete in real-world scenarios,
preparing them for future research or industry roles in algorithm design
and analysis. By the end of the course, students will have gained
practical experience in solving complex problems related to graph
structures and parameterized algorithms, positioning them to excel in
competitive programming environments.
in two prominent algorithmic challenges: the Graph Drawing Challenge and
the Parameterized Algorithms and Computational Experiments (PACE)
Challenge. The course aims to deepen students' understanding of graph
theory, algorithm design, and fixed-parameter tractability (FPT).
Students create a visualization of a given network of medium to large scale. The group is divided into several teams for this purpose. During the process, students acquire knowledge in the areas of network analysis, graph drawing and information visualization and practice applying this knowledge. Students also use suitable visualization software packages to create the visualization. The students present the current state of development at regular intervals and give each other feedback.
Throughout the course, students will develop and refine their
algorithms, receive feedback, and compete in real-world scenarios,
preparing them for future research or industry roles in algorithm design
and analysis. By the end of the course, students will have gained
practical experience in solving complex problems related to graph
structures and parameterized algorithms, positioning them to excel in
competitive programming environments.
Inhaltliche Voraussetzungen
Programming experience, data structures and algorithms/
Lehr- und Lernmethoden
There is one physical meeting of 90 minutes each week, but a large part of the actual work takes place in self-study, in which relevant literature is read and practical work is carried out on the project. At the weekly meetings, students present the results of their self-study.