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.

Learning outcomes

In order to pass, the student must be able to:

• model a combinatorial problem using a solver-independent constraint modelling language
• discuss various models of a combinatorial problem expressed in a constraint modelling language
• describe and compare different constraint solving technologies that can be used by the back-end solvers to a constraint modelling language, including constraint programming, local search, Boolean satisfiability (modulo theories), and integer programming
• decide which constraint solving technologies to try first when facing a new combinatorial problem, and motivate this decision
• design and evaluate different models of a combinatorial problem for various constraint solving technologies.

Content

The course focuses on modelling optimisation problems. The models can then be used to solve problems using an off-the-shelf solver.

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 to solve them. The course covers combinatorial (satisfaction or optimisation) problems, a constraint modelling language, the main characteristics of various constraint solving technologies, heuristics and good practice in modelling and solving combinatorial problems, examples of applications of combinatorial problem solving.

Instruction

Lectures, help sessions and solution sessions.

Assessment

Oral and written assignment presentations, 2+3 credits.