After successful completion of the course, students are able to understand the contents of this course. Among other effects, this understanding forms the basis for the capability to
correctly reproduce the statements and notions covered in the course as well as for the ability to explain and apply the proof techniques used in the course.

This lecture is an introduction to the design and analysis of algorithms for
students of mathematics The structure of the lecture mostly follows the central
design principles for algorithms, such as divide-and-conquer, dynamic
programming, or greedy algorithms. In order to achieve a well-balanced
presentation of the subject, graph theory and data structures play an important
role throughout the lecture. The mathematical foundations of discrete
algorithms, such as elementary combinatorics or recurrence relations, will be
treated thoroughly. In the choice of concrete algorithms mathematical tasks,
such as matrix multiplication, linear optimisation, or geometric problems, have
been preferred.

Presentation of the subject of the lecture.

The lecture starts on Monday, October 4.

Written exam

Es wird ein Skriptum zu Verfügung gestellt werden. Als ergänzende Literatur können empfohlen werden:

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. MIT Press, 3rd edition, 2009
2. Jon M. Kleinberg and Éva Tardos. Algorithm design. Addison-Wesley, 2006
3. Markus Nebel. Entwurf und Analyse von Algorithmen. Springer Vieweg, 2012
4. Thomas Ottmann and Peter Widmayer. Algorithmen und Datenstrukturen, 5. Auflage.
   Spektrum Akademischer Verlag, 2012
5. Sanjoy Dasgupta, Christos H. Papadimitriou, and Umesh V. Vazirani. Algorithms. McGraw-
   Hill, 2008

Basic mathematical knowledge (Analysis 1, Linear Algebra and Geometry 1) and basic programming experience (Introduction to Programming).