Algorithms and Data Structures 1

• Describe an algorithm in an understandable way, i.e., accurately, concise and unambiguous.
• Argue for the correctness of algorithms.
• Analyze algorithms, including being able to determine the running time and space consumption in asymptotic notation.
• Identify and formulate the underlying algorithmic problem in a given problem.
• Apply basic data structures like stacks, queues, linked lists and hash tables.
• Use graphs to model a given problem.
• Apply and analyze basic graph algorithms, for example BFS, DFS and Dijkstra’s algorithm.
• Analyze, evaluate, and compare algorithms / data structures and in the light of this, select an appropriate algorithm / data structure for solving a given problem.
• Modify known algorithms to solve a given problem.
• Describe and compare different algorithmic paradigms, including recursion, greedy algorithms, and divide-and-conquer.
• Implement and test data structures and algorithms, and make appropriate tests and empirical analysis of them.
• In a clear understandable language argue for choices made when solving a problem.

The concept of algorithms, graphs and trees, recursion and iteration, fundamental algorithms for sorting of data, techniques to analyze the complexity of algorithms (running time analysis, analysis of space usage), O-notation, etc. Elementary data structures (stacks, queues, linked lists, etc.), advanced data structures (heaps, binary search trees, etc.), graph algorithms (BFS, DFS, topological ordering, etc.), greedy algorithms, and divide-and-conquer algorithms.

02002/02003/02100/02101/02102/01017/01019, An introductory course in programming + an introductory course in discrete mathematics. Or similar competences.

Lectures and exercises. Teaching will be available in both English and Danish as far as possible.