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 2023 in Semester 1 at Clayton. It has no prerequisites and unlocks 4 units.

Credit points
6
Offered in 2023
Semester 1
Clayton
Assessment
Exam 60%
and 1 other task

This is the 2023 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 MTH2051 or MTH3051 or if you are enrolled in the Bachelor of Applied Data Science MTH2019 or both of (MTH2010 or MTH2015) and (MTH2021 or MTH2025) or if you are enrolled in the Honours of 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 methods from operations research and computational mathematics for continuous optimisation problems. A range of such optimisation problems arise 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, network optimisation and iterative methods for unconstrained nonlinear optimisation. You will also 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 optimisation 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 2023

Teaching periodCampusMode
First semesterClaytonOn campus

Assessment

  • Continuous assessmentOther
    40%
  • Examination (3 hours and 10 minutes)ExamThreshold hurdle
    60%

Learning outcomes

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

  1. 1

    Demonstrate an understanding of necessary and sufficient optimality conditions for constrained and unconstrained nonlinear optimisation problems;

  2. 2

    Demonstrate an understanding of the mathematical principles behind common iterative algorithms for solving unconstrained nonlinear optimisation problems;

  3. 3

    Analyse the relative benefits and drawbacks of iterative algorithms for unconstrained nonlinear optimisation problems;

  4. 4

    Implement and apply iterative algorithms for unconstrained nonlinear optimisation problems;

  5. 5

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

  6. 6

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

  7. 7

    Apply duality theory to prove optimality of a solution of a linear programming problem;

  8. 8

    Solve network optimisation problems with specialised algorithms.

Workload and teaching

  • Lectures36 hours
  • Applied sessions22 hours
  • Teaching approachActive learning
  • Three 1-hour lectures;
  • 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

For computational exercises, a Jupyter Server is available for students to use at https://maxima.erc.monash.edu/

Where it fits

MTH3330 is part of 7 areas of study in the 2023 handbook.

Contacts

Chief Examiners
Dr Janosch Rieger
Unit Coordinators
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 2023, 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.

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 1
Study abroad
Available