MonMap
A course mapper by Monash Association of Coding (MAC)
Optimisation and operations research
MTH3330
Synopsis
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.
Sourced from the Monash Handbook 2026.
Quick facts
- Credit points
- 6
- Level
- 3
- Audience
- Undergraduate
- Type
- Coursework
- School
- Faculty of Science
- Faculty
- School of Mathematics
- Handbook year
- 2026
Prerequisites (3)
What it unlocks (2)
- Discrete optimisationMTH4333
- Discrete optimisationMTH5333
Offerings (2)
- First semesterClayton · ON-CAMPUS / Malaysia · ON-CAMPUS
Listed in 8 areas of study
- Applied mathematicsLevel 3 units
- Applied mathematicsLevel 3 units
- Business analyticsAdditional business analytics units
- Financial and insurance mathematicsFinancial and insurance mathematics elective unit
- MathematicsMathematics elective units
- MathematicsMathematics elective units
- Pure mathematicsPure mathematics elective units
- Mathematical statisticsAdditional elective units