Learning Outcomes:

On completion of this module, students should be able to:
1. Understand the basic optimisation techniques
2. Gain a fundamental understanding of continuous optimisation and gradient-based approaches
3. Model real-world problems in terms of linear programming and integer linear programming
4. Competently apply the basic optimisation techniques to solve problems in various domains, including machine learning

Indicative Module Content:

Fundamentals of Optimisation
Numerical and gradient-based optimisation
Convex Optimisation
Linear Programming (Simplex Method)
Integer Programming
Mixed Integer Linear Programming
Meta-heuristics (e.g., Genetic programming, Tabu search)
Combinatorial Optimisation
Constraint Satisfaction Problems

Student Effort Type	Hours
Lectures	20
Tutorial	16
Autonomous Student Learning	90
Total	126

Not applicable to this module.

Not applicable to this module.
 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Continuous Assessment: There will be assignments to assess if a student is able to model a real-world problem into a linear program or integer linear program and then use a solver to solve that formulation.	Throughout the Trimester	n/a	Alternative linear conversion grade scale 40%	No	60
Examination: An in-person end of trimester examination is currently planned for this module. These arrangements are subject to COVID-19 public health advice and may change during the trimester.	2 hour End of Trimester Exam	Yes	Alternative linear conversion grade scale 40%	No	40

 

Resit In	Terminal Exam
Spring	Yes - 2 Hour

• 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.

"Algorithms for Optimization" by Mykel J. Kochenderfer and Tim A. Wheeler (https://mitpress.mit.edu/books/algorithms-optimization)
"Combinatorial Optimization" by Papadimitriou and Steiglitz
"A Gentle Introduction to Optimization" by B. Guenin, J. Konemann, J. Tuncel. Publisher: Cambridge University Press
"Combinatorial Optimization: Theory and Algorithms" by Korte and Vygen

Name	Role
Wenyi Sun	Tutor
Dena Tayebi	Tutor

Autumn
Tutorial	Offering 1	Week(s) - Autumn: All Weeks	Fri 12:00 - 12:50
Lecture	Offering 1	Week(s) - Autumn: All Weeks	Thurs 17:00 - 17:50
Lecture	Offering 1	Week(s) - Autumn: All Weeks	Tues 11:00 - 11:50
Autumn