ITO5216 · Discrete optimisation

Overview

This unit introduces the fundamentals of modelling for discrete optimisation, focusing on how to rigorously express a discrete optimisation problem in a manner that can be solved. Some topics covered will include decision variables, basic constraints, modelling with sets, modelling with functions, multiple modelling viewpoints, debugging discrete optimisation models as well as the fundamental algorithms for solving discrete optimization problems.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

In-semester assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. model a discrete optimisation problem using a mix of basic and more advanced modelling techniques in a high level modelling language;
2. interpret and explain models written by others;
3. explain how models are solved with different solving technologies;
4. identify and fix errors in models;
5. evaluate the limitations, appropriateness and benefits of different modelling patterns for common problem classes;
6. evaluate and improve the efficiency of models by applying different model transformations.

Workload

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Student experience, not official rules. Nothing here changes what the Handbook says.