After successful completion of the course, students are able to perform the following tasks.

This module expands the students' algorithmic knowledge and skills that were already taught in the Algorithms and Data Structures module. Students learn advanced rigorous analysis and design methods for efficient algorithms and deepen their skills in various areas such as abstraction, proof methodology and algorithmic complexity analysis.

After successfully completing the module, the students have acquired the following skills.

• Understanding and describing the presented efficient algorithms
• Understanding and applying the underlying algorithmic methods
• Analysis and evaluation of advanced algorithms
• Abstraction and modeling of algorithmic and combinatorial problems
• Proofs of correctness and runtime complexity of algorithms
• Designing advanced efficient algorithms
• Self-organized and responsible development of more efficient solutions
• Mathematically precise description of own solutions and proof ideas
• Presenting and critically discussing own and third-party solutions

• Recap of fundamental aspects of algorithm complexity and analysis, O-notation, graph theory
• Matroids and Greedy Algorithms
• Approximability
• Lower bounds in algorithm analysis
• String algorithms
• Scheduling algorithms
• Algorithms for artificial intelligence problems

The learning activities include lectures, exercises in small groups (“whiteboard exercises”), and two written exams.

We assume the semester will be taught in-person (this applies to lectures, exercises as well as tests).

Group Exercises and Tests.

Completion of the Algorithms and Data Structure module.