Module manager: Dr Haiko Muller
Email: H.Muller@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2024/25
Students must have studied one of the below Pre-requisite modules
| COMP2711 | Algorithms I |
| COMP2721 | Algorithms II |
| MATH2230 | Discrete Mathematics |
| MATH2231 | Discrete Mathematics with Computation |
This module is not approved as a discovery module
A graph is a mathematical object defined by a set of vertices and a set of edges that connect these vertices. Many problems arising in diverse areas such as transportation, telecommunication, molecular biology, industrial engineering, etc. can be modelled by graphs. These problems then reduce to optimizing some graph parameter. Since the objects we are dealing with are finite, we can envision an algorithm that would list all the possibilities and go through them to find the desired optima. The problem with such an algorithm is its complexity, it grows exponentially with the input size. Even with relatively small input graph it would take the computer centuries to find the answer. The graphs used to model problems in say bioinformatics, or the problems related to the internet, are enormous in size, stressing the importance for the algorithms to be extremely efficient. To construct efficient algorithms, one needs to exploit the mathematical understanding of the underlying objects. In this module we introduce a wide range of graph problems, classify them with respect to their computational complexity, and demonstrate how these methods extend to other computational problems.
On completion of this module students should be able to:
- recognise real-world problems that can be modelled by graph problems considered in this module;
- understand and be able to use these algorithms;
- demonstrate how such algorithms are analysed;
- construct reductions to prove NP-hardness;
On completion of the year/programme students should have provided evidence of being able to:
-understand and demonstrate coherent and detailed subject knowledge and professional competencies some of which will be informed by recent research/scholarship in the discipline;
-deploy accurately standard techniques of analysis and enquiry within the discipline;
-demonstrate a conceptual understanding which enables the development and sustaining of an argument;
-appreciate the uncertainty, ambiguity and limitations of knowledge in the discipline
Network flows (Ford-Fulkerson Labelling Algorithm, Max Flow Min Cut Theorem)
Matchings (matchings in bipartite graphs, matchings in general graphs, Edmonds’ Blossom Algorithm, matchings and coverings, Hall’s Theorem, König-Egervary Theorem)
Complexity Theory (the classes P and NP, NP-completeness, polynomial-time many-one reductions, proofs of NP-completeness of independent set, vertex cover and clique problems)
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 22 | 1 | 22 |
| Tutorial | 10 | 1 | 10 |
| Private study hours | 68 | ||
| Total Contact hours | 32 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Taught session preparation: 18 hours
Taught session follow-up: 18 hours
Self-directed study: 7 hours
assessment activities: 23 hours
Attendance and formative assessment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Coursework 1 | 20 |
| Total percentage (Assessment Coursework) | 20 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) (S1) | 2.0 Hrs 0 Mins | 80 |
| Total percentage (Assessment Exams) | 80 | |
Resits will be assessed by exam only.
The reading list is available from the Library website
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team