COMP47790 Optimisation

Academic Year 2021/2022

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. 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 Hours: 
Student Effort Type Hours
Lectures

20

Tutorial

16

Autonomous Student Learning

90

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
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


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring 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)
"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
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
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