UnitLevel 3Undergraduate

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 2020 in Semester 1 at Clayton. It has no prerequisites and unlocks 4 units.

Credit points
6
Offered in 2020
Semester 1
Clayton

This is the 2020 handbook entry. See the 2027 entry.

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Requisites

Before MTH3330

No prerequisites or corequisites besides the enrolment rules below.

Enrolment rules

PREREQUISITE: You must have passed one of MTH2010, MTH2015, ENG2005 or MAT1830, and one of MTH2021, MTH2025, MTH2040 or MAT1841.  Alternatively you must be enrolled in the Master of Financial Mathematics or must be enrolled in Honours of Econometrics and must have passed ETC2440.

Additional prerequisite: From 2021 you must have passed one of MTH2051 or MTH3051.  This won't apply to students enrolled in the Master of Financial Mathematics or Honours of Econometrics.

Overview

This unit introduces some of the fundamental methods from operations research and computational mathematics for continuous optimisation problems. A range of such optimisation problems appear in economics, engineering, finance, business, data science and many other application areas. You will receive an introduction to the mathematical theory of continuous optimisation with a focus on linear programming methods and smooth non-linear programming. This will broadly include duality theory, the simplex method for linear programming, Lagrangian relaxation methods for dealing with constraints, quadratic programming, and some methods for more general non-linear problems including iterative approximation. You will learn to implement the computational methods efficiently, how to test their implementations for accuracy and performance, and to interpret the results. You will work on realistic models for applications in a variety of fields. Applications may include examples of supply chain optimisation, economic modelling (including shadow prices), product mix optimisation, portfolio optimisation, parameter estimation and machine learning.

Offerings in 2020

Teaching periodCampusMode
First semesterClaytonOn campus
First semester (Fully flex)ClaytonFlexible

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

    Formulate a range of operations research problems as linear programming problems, and be able to solve them computationally;

  2. 2

    Demonstrate an understanding how the most widely used linear programming algorithms work;

  3. 3

    Apply duality theory to prove optimality of a solution;

  4. 4

    Interpret the solutions of optimisation problems, including analysing sensitivity of solutions;

  5. 5

    Implement several iterative algorithms for solving constrained and unconstrained non-linear optimisation problems and understand the mathematics behind these;

  6. 6

    Formulate and solve general non-linear programs arising in engineering, data science and other areas.

Workload and teaching

  • Three 1-hour lectures
  • One 2-hour applied class per week (in a computer lab)

Where it fits

MTH3330 is part of 7 areas of study in the 2020 handbook.

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 4 units, including MTH4331, MTH4333, MTH5331 and MTH5333.

When is MTH3330 offered?

In 2020, MTH3330 runs in Semester 1 at Clayton.

Which majors and minors include MTH3330?

MTH3330 is part of Applied mathematics; Financial and insurance mathematics; Mathematical statistics; Mathematics; and Pure mathematics.

More details

Credit points
6
Level
3
Study level
Undergraduate
Faculty
Faculty of Science
Organisational unit
School of Mathematics
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Not available