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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

Contacts

Chief Examiner(s)

Dr Graeme Gange

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

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

1 - In-semester assessment

2 - Examination (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload