
At the end of the course the student is able to:
 use and explain elementary data structures like lists, heaps, binary trees, and priority queues
 use and explain elementary algorithms like sorting, traversing and updating data structures, and basic optimization problems
 analyse the time complexity of algorithms and operations on data structures, e.g. using the Master Theorem or recursions, and use dynamic programming
 use and understand the Euclidean algorithm, the “grand daddy of all algorithms” (Knuth), in particular its computational efficiency, and its relevance in applications such as, e.g., RSA public key encryption
 use, explain and design algorithms on graphs and networks, such as computation of shortest paths, spanning trees, maximum flows, stable matching and clustering problems
 solve secondorder linear recurrence relations using characteristic polynomials or generating functions


The first two weeks of the study unit “Algorithmic Discrete Mathematics” are devoted to the understanding of elementary data structures, and their use in the design and theoretical analysis of classic discrete algorithms. This includes basic principles and techniques to analyse the time and space complexity of algorithms, worstcase and averagecase. The data structures include heaps, binary trees, as well as priority queues. Algorithms that are dealt with are for sorting, computational problems with permutations, the Euclidean algorithm to compute the greatest common divisor, the computation of shortest paths, minimum spanning trees. General algorithmic techniques that are introduced are divide and conquer, as well as dynamic programming. Some of these algorithms are implemented using the Python programming language, as part of the graph isomorphism implementation project.
The third and fourth weeks are devoted to structural, algorithmic, and combinatorial problems that lie in the core of discrete and combinatorial mathematics: Students understand core algorithmic techniques in discrete optimization, next to shortest paths and spanning trees also algorithms for network flows, stable matchings and clustering, the algorithmic power of duality on the example of maximum flows and minimum cuts, and learn how to solve combinatorial counting problems by means of (secondorder, linear) recurrence relations using the characteristic polynomial.





Bachelor Applied Mathematics 
Bachelor Technical Computer Science 
  Required materialsBookDiscrete and Combinatorial Mathematics: An Applied Introduction, Ralph P. Grimaldi, Pearson, 2003 (5th ed.). ISBN: 9780201726343 
 CanvasLecture Notes to be made available online 

 Recommended materialsInstructional modesAssessmentPresence duty   Yes 
 Assignment
 Lecture
 Q&A
 Tutorial

 TestsAlgorithmic Discrete Mathematics


 