Combinatorial Optimisation and Constraint Programming

10 credits

Course, Master's level, 1DL442

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
2 September 2024–19 January 2025
Language of instruction
English
Entry requirements

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

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 24,167
  • Total tuition fee: SEK 24,167

Read more about fees.

Application deadline
15 April 2024
Application code
UU-11009

Admitted or on the waiting list?

Registration period
26 July 2024–9 September 2024
Information on registration from the department

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
2 September 2024–19 January 2025
Language of instruction
English
Entry requirements

120 credits including Basic Course in Mathematics, Algebra I, and 10 credits 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
26 July 2024–9 September 2024
Information on registration from the department

About the course

  • The use of tools for solving a combinatorial problem, by first modelling it in a solving-technology-independent constraint-based modelling language and then running the model on an off-the-shelf solver.
  • Constraint consistency; constraint propagator; propagation fixpoint algorithm.
  • Solving by systematic search: construction and exploration of a search tree; branching strategies; handling of an objective function for optimisation.
  • Solving by (constraint-based) stochastic local search: construction and exploration of a search space; constraint violation; variable violation; move probing; search heuristics; search meta-heuristics.

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