| Unit name | Stochastic Optimisation |
|---|---|
| Unit code | MATHM6005 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Tadic |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Stochastic optimisation covers a broad framework of problems at the interface of applied probability and optimisation. The main focus of this unit is on Markov decision processes and game theory. Markov decision processes describe a class of single decision-maker optimisation problems that arise when applied probability models (eg Markov chains) are extended to allow for action-dependent transition distributions and associated rewards. Game theory problems are more complex in that they involve two or more decision makers (players), so the optimal action for each player will depend on the actions of other players. Here, we focus on Nash equilibria - strategies that are conditionally optimal in the sense that a player can not do do better by changing their own strategy while other players stay with their current strategy.
