Available in 2022
Course code



10 units


3000 level

Course handbook


From finance and health to science and engineering, optimisation has many applications and is at the heart of several modern technologies such as machine learning for big data and deep neural networks. This course develops the student’s ability to understand and apply the fundamental analytical, computational and statistical techniques for optimising deterministic and stochastic problems in practice.

The first part of the course deals with deterministic optimisation problems where all parameters are known, including linear and nonlinear programs. In practice, however, we often encounter systems, for which parameters are uncertain. The focus of the second part of the course is on methods for optimising stochastic systems, particularly where the dynamic of the system is governed by a Markov chain, such as in supply chains and queueing networks.

The written assignments give students the opportunity to apply the concepts they learn in lectures and labs to a number of theoretical and computational problems. The topics align with the course content covered by that stage of the semester.

The project gives students the opportunity to experience applying the concepts they learn in the course to a more applied problem as a teamwork. The project output involves a written report and verbal presentation.

Availability2022 Course Timetables


  • Semester 1 - 2022

Learning outcomes

On successful completion of the course students will be able to:

1. Formulate real-world problems in the mathematical language of optimisation.

2. Solve problems using analytical and computational techniques.

3. Interpret solutions of optimisation problems as they apply to scientific, financial and industrial applications.

4. Optimise systems under uncertainty using analytical and computational techniques.

5. Apply, as part of a team, optimisation and stochastic modelling to industry, business, engineering, psychology, health and broader scientific fields.


  • Foundations of Optimisation
  • Unconstrained Optimisation
  • Nonlinear Optimisation
  • Markov Decision Processes
  • Stochastic Dynamic Programming
  • Optimisation under Uncertainty for Big Data

Assumed knowledge

One course from: MATH1120 or MATH1220


one course from: STAT1300 or STAT2110 or STAT1070.

Assessment items

Formal Examination: Formal examination

Written Assignment: Written assignments

Project: Project

Contact hours


Computer Lab

Face to Face On Campus 2 hour(s) per Week for 12 Weeks starting in week 1


Face to Face On Campus 2 hour(s) per Week for 12 Weeks starting in week 1

The University of Newcastle acknowledges the traditional custodians of the lands within our footprint areas: Awabakal, Darkinjung, Biripai, Worimi, Wonnarua, and Eora Nations. We also pay respect to the wisdom of our Elders past and present.