After successful completion of the course, students are 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.
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.
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.
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.
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.
