Overview

This unit introduces the fundamental algorithms for solving discrete optimization problems, such as constraint programming, boolean satisfiability, mixed integer linear programming and local search.

Offerings

S2-01-CLAYTON-ON-CAMPUS
T3-57-OS-CHI-SEU-ON-CAMPUS

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Dr Graeme Gange

Learning outcomes

On successful completion of this unit, you should be able to:
1.

design efficient solutions for discrete optimisation problems;

2.

evaluate the limitations, appropriateness and benefits of different solving technologies for particular discrete optimisation problems;

3.

define and explore different complete and local search strategies for solving a given problem;

4.

explain how modelling interacts with solving technologies, and formulate models to take advantage of this using state of the art optimisation tools.

Teaching approach

Active learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assignment 1
2 - Assignment 2
3 - Mid-semester test
4 - Assignment 3
5 - In-class participation
6 - Scheduled final assessment (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories
Lectures

Workload requirements

Workload

Learning resources

Technology resources