MTH4333 - Discrete optimisation

There is a more recent version of this academic item available.

Overview

This unit provides an introduction to optimisation over discrete domains using integer programming and combinatorial methods. Discrete optimisation is frequently used to model decision problems in business and industry. This unit covers some of the mathematical tools required to solve these types of problems in practice. Building on linear programming, … For more content click the Read More button below.

This unit provides an introduction to optimisation over discrete domains using integer programming and combinatorial methods. Discrete optimisation is frequently used to model decision problems in business and industry. This unit covers some of the mathematical tools required to solve these types of problems in practice. Building on linear programming, the unit will cover dynamic programming, branch-and-bound, polyhedral analysis, decomposition methods and an introduction to heuristic search for combinatorial optimisation problems.

Offerings

S1-01-CLAYTON-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Professor Andreas Ernst

Unit Coordinator(s)

Professor Andreas Ernst

Notes

This unit is offered in alternate years commencing Semester 1, 2021

Teaching approach

Active learning

The unit will be taught in 3 hours of lectures per week with a one hour applied class. The lectures will cover the theory underpinning discrete optimisation algorithms and provide some guidance as to how to apply this theory. The applied class and assignments will give you a chance to apply the theory to new problems and to gain hands on experience in solving discrete optimisation problems computationally.

Assessment

1 - Three assignments

2 - Examination (3 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Applied sessions

Lectures

Workload requirements

Workload

Learning resources

Technology resources

Availability in areas of study

Master of Mathematics