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Stochastic Optimisation (MAST90144)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
Availability | Semester 1 |
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Fees | Look up fees |
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: 8 November 2024