The Physics of Algorithms

Course content

To provide the students with a toolbox of optimization and modeling algorithms along with a sense of which ones work best in a given situation 
To inspire the students to make use of analogies between physics and optimization and develop new ones

Recent methods are covered where physics has contributed significantly to the understanding and development of algorithms. Examples include Monte Carlo type algorithms like simulated annealing and genetic algorithms as well as maximum entropy solutions, information theory, and neural nets. A number of such algorithms will be presented theoretically as well as in practice, and the connections between physics and optimization will be emphasized. Students will get hands-on experience with implementing the methods during the exercise sessions. Students are expected to put serious effort into these implementations.


MSc Programme in Physics

MSc Programme in Physics w. minor subject

Learning outcome

The students have completed the course in full when they can:

  • Identify analogies between physical phenomena and optimization
  • Select and use optimizations methods for a particular problem and argue for their choice
  • Identify optimization opportunities in their own field of research

Through this course the student will learn about modeling algorithms, optimization, Monte Carlo calculations, information theory, neural nets, a.o. Emphasis will be on understanding the relationship between physics and optimization. The students will also learn that many traditional physics laws are really optimized outcomes of particular objective functions.

The student will at the end of this course be able to understand algorithms and optimization, see their relation to physics, and especially use these techniques within their own field of research.

Mixture of lectures and exercises.

Course notes and excerpts from articles and books are available on the course webpage for registered students.

Contents of the first year of the physics bachelor program including supporting courses and programming skills.

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Oral examination, 30 min
Written assignment, ~4 weeks
Each student has a choice between two forms of exam:
- a traditional oral exam without preparation time.
- a term report of max. 15 pages about a personal project agreed on between the student and teacher.
The date for the examination and the due date of the report are the same.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
More internal examiners
Criteria for exam assessment

See learning outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 30
  • Theory exercises
  • 16
  • Theory exercises
  • 0,5
  • Preparation
  • 159,5
  • English
  • 206,0