Modelling for Combinatorial Optimisation
Course, Master's level, 1DL451
Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including Basic Course in Mathematics, Algebra I, 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.
- Application fee: SEK 900
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 17 April 2023
- Application code
- UU-11004
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration.
Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including Basic Course in Mathematics, Algebra I, 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
- 28 July 2023–4 September 2023
- Information on registration.
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.