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COMP47790

Academic Year 2025/2026

Optimisation (COMP47790)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Deepak Ajwani
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers various optimisation techniques, including convex optimisation (including gradient-based approaches), linear programming, mixed integer linear programming, meta-heuristics and combinatorial optimisation.

About this Module

Learning Outcomes:

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 used in AI techniques

Indicative Module Content:

Fundamentals of Optimisation
Linear Programming (Simplex Method)
Integer Programming
Constraint Programming
Approximation Algorithms
Meta-heuristics (e.g., Genetic programming, Simulated annealing)
Combinatorial Optimisation
Convex Optimisation (including gradient-descent, Newton method, AdaGrad and Adam method)

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

80
Lectures

24
Tutorial

24
Total

128

Approaches to Teaching and Learning:
Lectures; Active/task-based learning; Enquiry & problem-based learning

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (Open Book): Exam to assess if a student is able to model a real-world problem into an 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
20
 No
Participation in Learning Activities: Participation in Tutorial Activities; Students need to submit their notebooks at the end of the tutorial sessions Week 2, Week 4, Week 6, Week 8, Week 10, Week 12 Alternative linear conversion grade scale 40% No
10
 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

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

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)
"Understanding and Using Linear Programming" by Jiří Matoušek and Bernd Gärtner
"Combinatorial Optimization" by Papadimitriou and Steiglitz
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