COMP47790 Optimisation

Academic Year 2022/2023

This module is an introduction to basic optimisation techniques, including gradient-based approaches, linear programming, mixed integer linear programming, meta-heuristics and combinatorial optimisation.

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Curricular information is subject to change

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

Indicative Module Content:

Fundamentals of Optimisation
Linear Programming (Simplex Method)
Integer Programming
Meta-heuristics (e.g., Genetic programming, Tabu search)
Combinatorial Optimisation
Convex Optimisation (including gradient-based optimisation)

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning

90
Lectures

20
Tutorial

16
Total

126
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Assignment to assess if a student is able to model a real-world problem into a linear program and then use a solver to solve that formulation. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

25
Assignment: Assignment to assess if a student is able to model a real-world problem into integer linear program, use a solver to solve that formulation showing an understanding of how ILPs are solved Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

25
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

50

Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
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
Name Role
James Fitzpatrick Tutor
Mr Ryan O'Connor Tutor
Dena Tayebi Tutor
Mr Jiwei Zhang Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 12:50
Tutorial Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 13:00 - 13:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 13:00 - 13:50
Spring