Postgraduate Handbook 2026-2027
Module COMP53215: Algorithms and Complexity
Department: Computer Science
COMP53215: Algorithms and Complexity
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 5 | 15 | Available in 2026/2027 | Durham |
Tied To
Pre-requisites
- None
Corequisites
- None
Excluded Combinations
- None
Aims
- Provide knowledge and critical understanding of paradigms, and fundamental ideas behind algorithms.
- Provide knowledge and critical understanding of paradigms, fundamental ideas and methods relating to computational complexity.
- Discuss the design and analysis of efficient algorithms.
Content
- Algorithmic methods: e.g. divide and conquer, dynamic programming.
- Mitigating hardness: e.g. approximation, branch and bound, exact methods.
- Computational complexity: e.g. classes between L and EXPTIME, time and space hierarchy theorems, FPT and the W-hierarchy.
- Randomised Algorithms: e.g. QuickSort, randomised data structures.
- Probabilistic Methods: e.g. Monte Carlo methods, tail inequalities
Learning Outcomes
Subject-specific Knowledge
- By the end of this module, students should be able to demonstrate:
- a systematic understanding of core techniques for constructing algorithms.
- an advanced understanding of core concepts from computational complexity theory.
- an ability to design novel algorithms to solve specific complex problems.
Subject-specific Skills
- By the end of this module, students should be able to demonstrate:
- familiarity with state-of-the-art algorithms to solve complex challenges and understanding of computational complexity implications.
- competence to translate abstract problem descriptions into algorithmic formulations; competent and educated selection of appropriate solution algorithms.
Key Skills
- By the end of this module, students should be able to demonstrate:
- familiarity with advanced paradigms and modern algorithms underlying computer science applications, and their analysis
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable students to learn the core material relevant to the topic and problem classes enable students to develop their theoretical understanding and problem-solving skills.
- Summative assessments and formative exercises encourage students to focus their ability to independently analyse and solve problems as it relates to the topic of algorithms and complexity.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 8 | 1 per week | 2 hours | 16 | Yes |
| Lectures | 8 | 1 per week | 1 hour | 8 | Yes |
| Problem Classes | 1 | 1 per week | 2 hours | 8 | Yes |
| Preparation and Reading | 118 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 100% | In-Year Test | 40 minutes | 50% | |
| In-Year Test | 40 minutes | 50% |
Formative Assessment
Via problem classes.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.