This is a graduate-level course on algorithm design (and analysis). It covers a range of topics and techniques in approximation algorithms, sketching and streaming algorithms, and online algorithms.

Introduction: RAM machine, data structures; Algorithms: sorting, median, matrix multiplication, shortest paths, minimal spanning trees; Paradigms: divide & conquer, dynamic programming, greedy algorithms; Data Structures: search trees, dictionaries, priority queues; Complexity Theory: P and NP, NP-completeness, Cook's theorem, reductions, cryptography and zero-knowledge proofs.

The course provides the foundation of the design and analysis of algorithms. The material is introduced using classical algorithmic problems including graph problems. The necessary basic introduction to graph theory is provided as part of this course.

Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt.

Complex network models are random graphs that feature one or several properties observed in real-world networks (e.g., social networks, internet graph, www). Depending on the application, different properties are relevant, and different complex network models are useful. This course gives an overview over some relevant models and the properties they do and do not cover.

Students present current or classical results from theoretical computer science.

Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates.