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Modelling discrete optimisation problems
FIT5216
Synopsis
This unit introduces the fundamentals of modelling for discrete optimisation, focusing on how to rigorously express a discrete optimisation problem in a manner that is can be solved. Topics covered will include decision variables, basic constraints, modelling with sets, modelling with functions, multiple modelling viewpoints, modelling time, common modelling patterns, model translation, and debugging discrete optimisation models. We will examine complex real world problems and see how they can be translated so that they can be solved by modern discrete optimisation technology.
Sourced from the Monash Handbook 2026.
Quick facts
- Credit points
- 6
- Level
- 5
- Audience
- Postgraduate
- Type
- Coursework
- School
- Faculty of Information Technology
- Handbook year
- 2026
Prerequisites (5)
- Fundamentals of artificial intelligenceFIT5047
- Statistical data modellingFIT5197
- Machine learningFIT5201
- Deep learningFIT5215
- Planning and automated reasoningFIT5222
What it unlocks
Nothing in the visible graph depends on this unit.
Offerings (1)
- First semesterClayton · FLEXIBLE
Listed in 2 areas of study
- Computational scienceElective units
- Software engineeringSoftware engineering technical electives