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Curricular information is subject to change
On completion of this module, students should be able to:
1. Understand the basic optimisation techniques
2. Model real-world problems in terms of linear programming and integer linear programming
3. Competently apply the basic optimisation techniques to solve problems in various domains, including machine learning
4. Gain a fundamental understanding of convex optimisation and gradient-based approaches
Fundamentals of Optimisation
Linear Programming (Simplex Method)
Integer Programming
NP-hardness and Approximation Algorithms
Meta-heuristics (e.g., Genetic programming, Simulated annealing)
Combinatorial Optimisation
Convex Optimisation (including gradient-based optimisation)
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Tutorial | 24 |
| Autonomous Student Learning | 80 |
| Total | 128 |
Not applicable to this module.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Assignment(Including Essay): Assignment to assess if a student is able to model a real-world problem into a LP or ILP, use a solver to solve that formulation showing an understanding of how ILPs are solved. | Week 7, Week 8 | Alternative linear conversion grade scale 40% | No | 30 |
No |
| Assignment(Including Essay): Assignment to assess if a student is able to model a real-world problem into LP or ILP, use a solver to solve that formulation. Also, there can be some questions on convex optimisation. | Week 11, Week 12 | Alternative linear conversion grade scale 40% | No | 20 |
No |
| Exam (In-person): An in-person end of trimester examination consisting of longer questions to test the understanding of optimisation concepts as well as short answer questions | End of trimester Duration: 1 hr(s) |
Alternative linear conversion grade scale 40% | No | 50 |
No |
| Resit In | Terminal Exam |
|---|---|
| Summer | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Formative assessment in tutorial sessions; For the assignments, a group feedback will be provided post-assessment and an individual feedback will be posted on the Brightspace VLE later.
| Name | Role |
|---|---|
| Mr Jiwei Zhang | Tutor |