Unit name | Stochastic Optimisation |
---|---|
Unit code | MATHM0044 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Ayalvadi Ganesh |
Open unit status | Not open |
Pre-requisites |
MATH11300 Probability 1 (or MATH10013 Probability and Statistics) and MATH20008 Probability 2 |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
Lecturers: Vladislav Tadic and Ayalvadi Ganesh
Unit Aims
The unit deals with the study of optimisation under uncertainty. It introduces some of the main modelling frameworks within which a wide variety of such problems can be set, before going on to study algorithms for their solution, and the analysis of these algorithms.
Unit Description
Stochastic optimisation covers a broad framework of problems at the interface of applied probability and optimisation. The unit will cover both static and dynamic problems. Static problems involve the optimisation of functions whose values are available only through noise-corrupted observations. Dynamic problems involve sequential decision-making to optimise some measure of long-term reward in a stochastic system evolving over time. The two main models studied in this context will be multi-armed bandit problems and Markov decision processes.
The unit will emphasise theoretical analysis of algorithms and derivation of optimal algorithms, as well as applications.
Students who successfully complete this unit should be able to:
The unit will be taught through a combination of
90% Timed, open-book examination 10% Coursework
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
If you fail this unit and are required to resit, reassessment is by a written examination in the August/September Resit and Supplementary exam period.
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