MTH3330 - Optimisation and operations research

Overview

This unit introduces some of the fundamental concepts and algorithms of mathematical optimisation. Optimisation underpins many parts of both data analytics (machine learning) and business analytics (management science/operations research). The concepts and approaches taught in this unit will be illustrated using examples from both types of analytics, such as training ML models and planning models arising in supply chain optimisation. The unit provides an introduction to the mathematics of continuous optimisation with focus on iterative gradient descent methods, linear programming and network optimisation. It covers both the underpinning theory, such as convergence analysis and duality, and the practical implementation of optimisation algorithms.

Offerings

S1-01-CLAYTON-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Professor Andreas Ernst

Unit Coordinator(s)

Professor Andreas Ernst

Notes

You are required to bring your own device, such as a laptop, on which you can complete computational exercises, to the applied classes.

Teaching approach

Active learning

Assessment

1 - Continuous assessment

2 - Final assessment - Exam (3 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Applied sessions

Workshops

Workload requirements

Workload

Learning resources

Required resources

Technology resources

Other unit costs

Costs are indicative and subject to change.
Miscellaneous items required (Unit course reader, Printing, Stationery) - $120.

Availability in areas of study

Applied mathematics
Financial and insurance mathematics
Mathematical statistics
Mathematics
Pure mathematics