| Semester | |
| Module # | INF-ALG-10 |
| Programmes | Computer Science Bachelor, Business Information Systems Bachelor |
| IBR Group | ALG (Prof. Fekete) |
| Type | Lab |
| Lecturer | |
| Credits | 5 |
| Hours | 0+3 |
| Time & Place | We will have regular slots in which you can present your progress and get help. You do not have to attend the whole slot and also not every week, though, many students like to use these slots to work in a group and have the ability to directly ask questions. |
| Start | Kickoff: Tuesday, 2024-10-15, 16:45, IZ305. (please let me know if you cannot make it to the kickoff, and we may be able to find a solution.) |
| Prerequisites | For a successful experience in this course and to effectively work on the projects, students are expected to meet the following prerequisites:
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. |
| Language | German/English |
| Certificates | Wahlpflichtbereich (unbenotetes Praktikum). Wichtig: Du kannst an diesem Praktikum nur einmal teilnehmen, unabhängig davon, ob du es dir als Wahlpflicht oder Teamprojekt hast anerkennen lassen. |
| Registration | 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. |
| Content | Optimization challenges are pervasive across numerous real-world applications within computer science, ranging from route planning to job scheduling. Certain problems, like the shortest path, can be solved efficiently and optimally with a solid theoretical foundation. However, a significant number of these challenges are classified as NP-hard, indicating that, for these problems, there is no known algorithm capable of consistently solving every instance efficiently to proven optimality. In such instances, heuristic approaches, such as genetic algorithms, are frequently employed as practical solutions. Yet, the question arises: Is it possible to devise algorithms that yield optimal solutions within a feasible timeframe for reasonably sized instances? This laboratory course is dedicated to exploring three sophisticated techniques that hold the potential for computing optimal solutions for a vast array of problems within practical limits. These techniques include:
For algorithm engineers and operations researchers, mastering these techniques opens the door to modeling and solving a wide spectrum of combinatorial optimization problems. By the end of this course, you will have acquired the skills to leverage these powerful methodologies, enabling you to approach NP-hard problems not only with theoretical insight but with practical, actionable solutions. |
| References |
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