Official course description, subject to change:

Basic info last published 15/03-24
Course info
ECTS points:
Course code:
Participants max:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
Price for EU/EEA citizens (Single Subject):
10625 DKK
MSc. Master
MSc in Computer Science
Course manager
Professor, Deputy Head of Department
Course semester
EfterÄr 2024
26 August 2024
24 January 2025

This course is an advanced course on algorithms which builds on top of an introductory course on algorithms and data structures. The course focuses on advanced techniques for identifying and solving computationally hard problems and on how to adapt such techniques to real-world scenarios.


This course introduces students to techniques for solving complex programming tasks arising in modern IT systems. The student taking this course will be able to quickly identify and solve a certain class of problems that can arise among the tasks that a skilled programmer must solve.

Focus in the course is on algorithm design and identification of computationally hard problems. The course contains both theoretical and analysis and implementation exercises.

Contents of lectures includes: Formulating an algorithmic problem, greedy algorithms, graph algorithms, divide and conquer, dynamic programming, network flow, reductions.

Formal prerequisites
Before the course, the student should be able to: Perform basic analysis of algorithm correctness and complexity, using invariants and big-O notation. Use basic algorithms and data structures when programming (e.g., lists, queues, stacks, search trees, hashing, sorting algorithms, and basic graph algorithms). This can be achieved, for example, by taking the courses "Foundations of Computing - Algorithms and Data Structures" or "Algorithms and Data Structures" (BADS).
Intended learning outcomes

After the course, the student should be able to:

  • Solve a wide range of real-life programming problems in a scalable way by employing algorithmic design techniques and tools.
  • Identify and formulate precisely (if possible) the algorithmic problem underlying in a given programming task.
  • Apply the following algorithmic techniques when solving a problem: Greedy, divide and conquer, dynamic programming, reduction to network flow.
  • Look up suitable NP hardness results in a compendium, and perform simple reductions from such problems to establish NP hardness.
Ordinary exam
Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A33: Written exam on premises on paper with restrictions