This course is about algorithm theory and complexity theory, which includes the following topics: Graphs and graph algorithms, greedy algorithms, dynamic programming, linear programming, and NP-completeness.
Course description for study year 2023-2024. Please note that changes may occur.
Introduction to algorithm theory and complexity theory; Sorting and order statistics, datastructures , advanced design and analysis techniques, graphs and graph algorithms, multithreaded algorithms, NP-completeness.
After completing this course the student should be able to:
Understand what algorithms and datastructures mean for developing lage and complex information systems
Create efficient algorithms, in terms of time, and resource like memory
Choose and apply different types of algorithms depending on what the information systems demand
Choose the optimal algorithms among many competing ones
Required prerequisite knowledge
DAT200 Algorithms and Datastructures
Form of assessment
No printed or written materials are allowed. Approved basic calculator allowed
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.