Syllabus for Modelling for Combinatorial Optimisation
Modellering för kombinatorisk optimering
- 5 credits
- Course code: 1DL451
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2018-03-09
- Established by:
- Revised: 2018-08-30
- Revised by: The Faculty Board of Science and Technology
- Applies from: Spring 2019
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.
- Responsible department: Department of Information Technology
On completion of the course, the student should be able to:
- define the concept of combinatorial (optimisation or satisfaction) problem
- explain the concept of constraint, as used in a constraint-based modelling language
- model a combinatorial problem in a constraint-based solving-technology-independent modelling language
- compare (empirically) several models, say by introducing redundancy or by detecting and breaking symmetries
- describe and compare solving technologies that can be used by the backends to a constraint-based modelling language, including constraint programming, local search, Boolean satisfiability (modulo theories), and mixed integer programming
- choose suitable solving technologies for a combinatorial problem, and motivate this choice
- present and discuss topics related to the course content, orally and in writing, with a skill appropriate for the level of education
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.
Lectures, help sessions, solution sessions and project
Oral and written presentations of assignments (3 credits).
Oral and written presentations of a project (2 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
This course cannot be included in the same degree as 1DL448 Modelling for Combinatorial Optimisation or 1DL449 Constraint Modelling for Combinatorial Optimisation or 1DL441 Combinatorial Optimisation using Constraint Programming.
- Latest syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2018)
Applies from: Autumn 2021
Some titles may be available electronically through the University library.
The course has no required course book. Course material and references will be provided during the course.