192.135 Fixed-Parameter Algorithms and Complexity
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2023W, VU, 3.0h, 4.5EC


  • Semesterwochenstunden: 3.0
  • ECTS: 4.5
  • Typ: VU Vorlesung mit Übung
  • Format der Abhaltung: Präsenz


Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage...

After successful completion of the course, students will be able to understand the theory of parameterized complexity and fixed-parameter tractability in sufficient depth to read and follow latest developments in the area and, crucially, to analyze problems they encounter from the parameterized viewpoint. First and foremost, this includes the ability to obtain asymptotically efficient algorithms and strong lower bounds for problems of interest.

Inhalt der Lehrveranstaltung

Fixed-parameter algorithms provide a powerful approach for efficiently solving many NP-hard problems by exploiting structural aspects of problem instances in terms of a problem parameter. This course provides an overview of the main techniques for developing fixed-parameter algorithms (including bounded search trees, kernelization, color coding, modulators) as well as the fundamentals of parameterized complexity theory (such as the Weft-hierarchy, XP and para-NP-hardness, kernelization lower bounds) which allows to provide strong evidence that certain problems cannot be solved by a fixed-parameter algorithm.


The core of the course consists of a series of blocked lectures which explore advanced topics in the studied area. The lectures are held in an informal, seminar-like setting and are highly interactive - students are expected to actively engage in what's going on. Every new method and technique introduced during the lecture is demonstrated on several examples.


Schriftlich und Mündlich

Weitere Informationen

The course will be held in on-site blocked format if possible, and online blocked format otherwise. There will be six lecture blocks, and one additional exercise block.


Vortragende Personen


LVA Termine

Mo.13:00 - 16:3008.01.2024 - 22.01.2024Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Fr.13:00 - 16:3012.01.2024Seminarraum FAV 05 (Seminarraum 186) Lecture
Do.13:00 - 16:3018.01.2024 - 25.01.2024Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Mo.13:00 - 16:3029.01.2024Seminarraum FAV EG B (Seminarraum von Neumann) Exercises
Fixed-Parameter Algorithms and Complexity - Einzeltermine
Mo.08.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Fr.12.01.202413:00 - 16:30Seminarraum FAV 05 (Seminarraum 186) Lecture
Mo.15.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Do.18.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Mo.22.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Do.25.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Lectures
Mo.29.01.202413:00 - 16:30Seminarraum FAV EG B (Seminarraum von Neumann) Exercises


The grading is based on a two-step evaluation process. In the first and mandatory part, each student is expected to select a recent research paper (based on guidance from the lecturer) from the considered area, read it, and prepare a presentation of its contents. This is sufficient to pass the course with a basic grade. Students who want a good grade take an oral exam where they demonstrate their understanding of the topics covered in the lecture.


Von Bis Abmeldung bis
16.10.2023 01:00 09.01.2024 23:59


066 011 DDP Computational Logic (Erasmus-Mundus) Gebundenes Wahlfach
066 645 Data Science Keine Angabe
066 926 Business Informatics Keine Angabe
066 931 Logic and Computation Gebundenes Wahlfach
066 937 Software Engineering & Internet Computing Gebundenes Wahlfach
066 938 Technische Informatik Gebundenes Wahlfach


Es wird kein Skriptum zur Lehrveranstaltung angeboten.


This course requires basic knowledge on the design and analysis of algorithms as well as basic complexity theory. Knowledge of the topics covered in the Algorithmics course is an advantage.