Modelling for Combinatorial Optimisation

5 credits

Course, Master's level, 1DL451

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
3 November 2025–18 January 2026
Language of instruction
English
Entry requirements

120 credits including Basic Course in Mathematics and Algebra I or Basic Algebra, and 10 credits in 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.

Selection

Higher education credits in science and engineering (maximum 240 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 12,083
  • Total tuition fee: SEK 12,083

Read more about fees.

Application deadline
15 April 2025
Application code
UU-11005

Admitted or on the waiting list?

Registration period
20 October 2025–9 November 2025
Information on registration from the department

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
3 November 2025–18 January 2026
Language of instruction
English
Entry requirements

120 credits including Basic Course in Mathematics and Algebra I or Basic Algebra, and 10 credits in 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
20 October 2025–9 November 2025
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.

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