MTH4330 Optimisation and operations research
Faculty of Science
MTH4330 Optimisation and operations research is a level 4, 6-credit-point, undergraduate and postgraduate unit from the Faculty of Science, offered in 2026 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2026
- Semester 1
- Clayton
- Assessment
- Exam 50%
- and 1 other task
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before MTH4330
No prerequisites or corequisites besides the enrolment rules below.
After MTH4330
No unit lists MTH4330 as a prerequisite in the 2026 handbook.
Enrolment rules
Prohibition: MTH3330
You must be enrolled in the Graduate Certificate in Mathematics or the Master of Mathematics
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 in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Continuous assessmentDemonstration50%
- Final assessment - Exam (3 hours and 10 minutes)Examination50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Explain and apply the mathematical theory of optimisation, including optimality conditions, iterative algorithms for nonlinear problems, and the principles of duality and non-smooth optimisation;
- 2
Formulate and analyse optimisation problems arising in machine learning, operations research, and network optimisation, selecting and justifying appropriate algorithms;
- 3
Implement and evaluate linear programming and related optimisation algorithms, proving optimality where appropriate and applying them to real-world data and applications;
- 4
Communicate optimisation reasoning and results effectively, both orally and in writing, and collaborate in small groups to solve problems;
- 5
Extend and deepen understanding of time series methods through advanced model synthesis, rigorous analysis, and independent application to complex or novel datasets.
Workload and teaching
- Applied sessions22 hours
- Workshops36 hours
- Teaching approachActive learning
Two 1.5-hour workshops;
One 2-hour applied class (in weeks 2-12) and
7 hours of independent study per week.
Learning resources
Required resources
Most of the content of this unit is covered in the following two textbooks:
1. Luenberger & Ye, “Linear and nonlinear programming” 2016 (4th edition).
2. Nodeal & Wright "Numerical Optimization" 2006 (2nd edition)
Both of these are available electronically from the library.
Technology_resources: Computer for completing computational exercises
Contacts
- Chief Examiners
- Professor Andreas Ernst
- Unit Coordinators
- Professor Andreas Ernst
Common questions
What are the prerequisites for MTH4330?
MTH4330 has no prerequisites, but enrolment rules apply.
When is MTH4330 offered?
In 2026, MTH4330 runs in Semester 1 at Clayton.
Does MTH4330 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.