Modelling for Combinatorial Optimisation
Course, Master's level, 1DL451
Expand the information below to show details on how to apply and entry requirements.
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
120 credits including Basic Course in Mathematics and Algebra I or Basic Algebra, and a second course in computer programming or another combination of courses containing basic concepts in algebra, combinatorics, logic, graph theory, set theory and implementation of basic search algorithms. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2026
- Application code
- UU-11005
Admitted or on the waiting list?
- Registration period
- 19 October 2026–8 November 2026
- Information on registration from the department
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
120 credits including Basic Course in Mathematics and Algebra I or Basic Algebra, and a second course in computer programming or another combination of courses containing basic concepts in algebra, combinatorics, logic, graph theory, set theory and implementation of basic search algorithms. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 19 October 2026–8 November 2026
- Information on registration from the department
About the course
Combinatorial optimisation problems arise in many fields, for example, design and resource allocation in communication systems, motion planning for autonomous vehicles, verification and synthesis of chip circuits, scheduling of scientific experiments, design of cryptographic substitution functions, design of steel mill slabs, and identification of a minimum set of reactions to synthesise a given molecule. The course teaches the use of tools to solve hard combinatorial optimisation problems by first modelling them in a solver-independent constraint modelling language and then using an off-the-shelf constraint solver, as opposed to designing an (approximation) algorithm from first principles.
The theory and algorithms underlying the constraint solvers used in this course will not be explained in depth, as specialised courses exist for this purpose, hence the course is relevant for students in many research areas, not only computer science, especially nowadays that combinatorial problems become more and more central to many research activities. The modelling and analytical skills that are central to this course are also important on their own and can be applied to other types of problems.