Stochastic Optimisation (MAST90144)
Graduate courseworkPoints: 12.5Not available in 2025
About this subject
Overview
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Stochastic optimisation encompasses many diverse areas including control theory, reinforcement learning, multiarmed bandit problems, simulation optimisation, and neural networks. Stochastic optimisation can be succinctly described as sequential decision making under uncertainty. In a sequential decision problem, the system being modelled progresses through a finite or infinite number of stages. At each stage, the system is in a particular state taken from a discrete or continuous state space, and decision (action) is taken which may depend on the stage and/or state. The aim is to design a set of decisions or actions (a policy) at each stage, so that an objective function is optimised. Randomness is incorporated into the problem by exogeneous information that is only realised once a decision is made at each stage. Topics include Markov decision processes, approximate dynamic programming, reinforcement learning, simulation optimisation, and robust optimisation. This subject provides a rigorous mathematical treatment of stochastic optimisation, and will include applications selected from logistics, finance, transportation, health, resource allocation, e-commerce, and supply chain management.
Intended learning outcomes
On completion of this subject, students should be able to:
- demonstrate an understanding of the fundamental concepts and techniques for modelling, analysing, and solving problems that involve sequential decision making under uncertainty;
- demonstrate an understanding of the theory that underlies the methodologies that have been developed to solve problems in stochastic optimisation, from a rigorous mathematical perspective;
- apply specific techniques such as approximate dynamic programming, robust optimisation, and simulation-based optimisation to solve problems in stochastic optimisation;
- have the ability to develop and analyse decision making models in real-world situations where stochastic optimisation can be applied.
- pursue further studies in stochastic optimisation and related areas.
Generic skills
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 12 April 2025
Eligibility and requirements
Prerequisites
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30001 | Stochastic Modelling | Semester 2 (On Campus - Parkville) |
12.5 |
MAST30022 | Decision Making | Semester 2 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
MAST20004 Probability or MAST20006 Probability for Statistics, and MAST20018 Discrete Maths and Operations Research
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 12 April 2025
Assessment
Description | Timing | Percentage |
---|---|---|
Up to 40 pages of assignments (4 written assignments worth 10% due in weeks 5, 7, 9, and 11).
| Throughout the teaching period | 40% |
A three-hour written examination, worth 60% of the mark, during the examination period
| During the examination period | 60% |
Last updated: 12 April 2025
Dates & times
Not available in 2025
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 12 April 2025
Further information
- Texts
Prescribed texts
None.
Recommended texts and other resources
Powell, W. B., Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions. Wiley-Interscience, 2022.
Powell, W. B., Sequential Decision Analytics and Modeling: Modeling with Python. Now Foundation and Trends, 2022.
Birge, J. R. and Louvreau, F. Introduction to Stochastic Optimisation, 2nd edition. Springer Science, 2011.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 12 April 2025