MTH3330 Optimisation and operations research
Faculty of Science
MTH3330 Optimisation and operations research is a level 3, 6-credit-point, undergraduate unit from the Faculty of Science, offered in 2024 in Semester 1 at Clayton. It has no prerequisites and unlocks 2 units.
- Credit points
- 6
- Offered in 2024
- Semester 1
- Clayton
- Assessment
- Exam 60%
- and 1 other task
This is the 2024 handbook entry. See the 2027 entry.
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Requisites
Before MTH3330
No prerequisites or corequisites besides the enrolment rules below.
After MTH3330
2 units list MTH3330 as a prerequisite or corequisite.
Enrolment rules
PREREQUISITE: You must have passed one of MTH2051 or MTH3051 or both of (MTH2010 or MTH2015) and (MTH2021 or MTH2025).
If you are enrolled in the Bachelor of Applied Data Science or Bachelor of Applied Data Science Advanced (Honours) you must have passed MTH2019.
If you are enrolled in the Honours in Econometrics you must have passed ETC2440 (this requires manual enrolment).
Alternatively you must be enrolled in the Master of Financial Mathematics, the Master of Mathematics, or receive special permission from the unit coordinator.
FROM 2025
You must have passed one of MTH2051 or MTH3051 or if you are enrolled in the Honours in Econometrics, ETC2440 (this requires manual enrolment).
Alternatively you must be enrolled in the Master of Financial Mathematics, the Master of Mathematics, or receive special permission from the unit coordinator.
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 2024
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Continuous assessmentOther40%
- Examination (3 hours and 10 minutes)ExamThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Demonstrate an understanding of necessary and sufficient optimality conditions for optimisation problems;
- 2
Demonstrate an understanding of the mathematical principles behind common iterative algorithms for solving unconstrained nonlinear optimisation problems;
- 3
Formulate as an optimisation problem the task of training a machine learning model and select an appropriate optimisation algorithm;
- 4
Implement and apply iterative algorithms for unconstrained nonlinear optimisation problems;
- 5
Formulate a range of operations research problems as linear programming problems, and solve them computationally;
- 6
Demonstrate an understanding how the most widely used linear programming algorithms work;
- 7
Apply duality theory to prove optimality of a solution of a linear programming problem;
- 8
Solve network optimisation problems with specialised algorithms.
Workload and teaching
- Workshops36 hours
- Applied sessions22 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.
Active learning will occur in lectures and applied classes.
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.
Where it fits
MTH3330 is part of 7 areas of study in the 2024 handbook.
- APPLMTH05Applied mathematics elective unitsApplied mathematicsNo reviews yet
- APPLMTH07Applied mathematics elective unitsApplied mathematicsNo reviews yet
- FININMAT04Financial and insurance mathematics elective unitFinancial and insurance mathematicsNo reviews yet
- MTHSTAT07Mathematical statistics elective unitsMathematical statisticsNo reviews yet
- MATHS09Mathematics elective unitsMathematicsNo reviews yet
- MATHS11Mathematics elective unitsMathematicsNo reviews yet
- MATPURE11Pure mathematics elective unitsPure mathematicsNo reviews yet
Contacts
- Unit Coordinators
- Professor Andreas Ernst
- Chief Examiners
- Professor Andreas Ernst
Common questions
What are the prerequisites for MTH3330?
MTH3330 has no prerequisites, but enrolment rules apply.
What can I take after MTH3330?
MTH3330 is a prerequisite or corequisite for 2 units, including MTH4333 and MTH5333.
When is MTH3330 offered?
In 2024, MTH3330 runs in Semester 1 at Clayton.
Does MTH3330 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 1 other task.
Which majors and minors include MTH3330?
MTH3330 is part of Applied mathematics; Financial and insurance mathematics; Mathematical statistics; Mathematics; and Pure mathematics.