Algorithmikpraktikum: Solving NP-hard Problems in Practice

For a successful experience in this course and to effectively work on the projects, students are expected to meet the following prerequisites:

1. Proficiency in Python: The course's programming components will be exclusively conducted in Python. It is essential that you have a solid grasp of Python, as there will not be sufficient time to learn the language during the course.
2. Algorithmic Foundations:
   • Completion of Algorithms and Data Structures 1 is compulsory for foundational knowledge.
   • It is advisable to have also completed (or complete in parallel) Algorithms and Data Structures 2 and Network Algorithms to be better prepared for the more complex topics.
   • Additionally, it is beneficial to have attended Logic for Computer Scientist and Theoretical Computer Science I+II to be familiar with NP-hardness and propositional logic.
3. Unix-Based Operating System:
   • Access to a Unix system, which could be in the form of a virtual machine, is required for the course. Students should possess a fundamental understanding of Unix command-line operations.
   • While most of the tools and software used in this course are compatible with Windows, support for Windows-specific issues cannot be guaranteed.
4. Version Control with Git:
   • A basic familiarity with Git is needed for version control purposes. While Git skills can be acquired swiftly, students are expected to learn them independently prior to or during the initial phase of the course.

Please ensure you meet these requirements to engage fully in the course activities. If you have any questions or need clarification on the prerequisites, feel free to reach out to us.

To register for this module, please sign up for the mailing list. At the beginning of the semester, you will get a mail with the details for kickoff meeting. You can also just show up at the kickoff meeting, but a registration on the mailing list is still recommendable.

This course can either be attended as normal practical training (unbenoteter Wahlpflichtbereich) or as team project.

• Discrete Optimization Course: For those who are want to dive really deep into the topic, I recommend doing this free course on Coursera in parallel. It is very intense but also very rewarding. Probably one of the best courses I have ever seen.
• In Pursuit of the Traveling Salesman: This amazing book is not a surprisingly good read, but also a great introduction to the field of optimization. It gives you a lot of the ideas that allow us to solve NP-hard problems in practice, while also gently introducing you to the way of thinking of an optimization expert.