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COMP30930

Academic Year 2026/2027

Optimisation (COMP30930)

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

Curricular information is subject to change.

This module is an introduction to basic optimisation techniques, including linear programming, integer linear programming, convex optimisation, 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

Indicative Module Content:

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

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Competent

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Competent

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

Ensure access to affordable, reliable, sustainable and modern energy for all 1

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation 1
 

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 Component repeat (in-module) Offered
Assignment(Including Essay): Assess the student's ability to model complex real-world problems as LP/ILP, solve them using a solver, and demonstrate a deep understanding of convex optimisation techniques. Week 11 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
Participation in Learning Activities: Participation in Tutorial Activities; Students need to submit their notebooks at the end of the tutorial sessions Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 8, Week 9, Week 10 Alternative linear conversion grade scale 40% No
10
 No
Exam (In-person): An 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 Alternative linear conversion grade scale 40% No
20
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
No
 

Resit In Terminal Exam
Summer 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

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, 23, 24, 25, 26, 30, 31, 32, 33 Mon 10:00 - 11:50
Spring Tutorial Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 13:00 - 14:50
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