Modelling and Problem Solving

The course covers major (combinatorial) solving approaches, namely constraint programming, integer programming, boolean satisfiability, dynamic programming and decision diagrams, and local search. The course focuses on modelling practical problems, expressing them in the surveyed paradigms; algorithms for solving such problems are covered in the follow-up "Constraint Solving" course. The topic of genetic algorithms is reserved for the course "Evolutionary Algorithms". Lastly, the course discusses conducting proper empirical evaluations. 

The final grade of the course consists of the following components:

• Digital Exam (weighting 60%)

• Group Assignment 1: lab assignment (weighting 10%)

• Group Assignment 2: lab assignment (weighting 10%)

• Group Assignment 3: lab assignment (weighting 10%)

• Group Assignment 4: lab assignment (weighting 10%)

Final grade calculation = 0.6 * Digital Exam + 0.1 * Group Assignment 1 + 0.1 * Group Assignment 2 + 0.1 * Group Assignment 3 + 0.1 * Group Assignment 4

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A - Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Digital Exam: Resit opportunity

• Group Assignment 1: Repair opportunity

• Group Assignment 2: Repair opportunity

• Group Assignment 3: Repair opportunity

• Group Assignment 4: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).