MA428 Half Unit
Combinatorial Optimisation
This information is for the 2020/21 session.
Teacher responsible
Katerina Papadaki
Availability
This course is available on the MSc in Applicable Mathematics and MSc in Operations Research & Analytics. This course is available with permission as an outside option to students on other programmes where regulations permit.
Pre-requisites
Some familiarity with graph theory and some knowledge of linear programming is desirable. For students that have no linear programming background, it is recommended that they read the material of the first four lectures of course MA423, which can be found on the Moodle page of MA423.
Course content
The course is intended as an introduction to discrete and combinatorial techniques for solving optimisation problems, mainly involving graphs and networks. Topics covered include: minimum spanning trees, with a brief introduction to matroids; shortest path algorithms; maximum flow algorithms; minimum cost flow problems; matching and assignment problems; and other topics that may vary from year to year.
Teaching
This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Lent Term. Lectures and solutions to exercices will be delivered as online videos; classes will be delivered as a combination of virtual and on campus question and answer sessions.
Formative coursework
Students will be given weekly exercises. Oral feedback will be provided in the virtual/on campus weekly classes (question and answer sessions), where the weekly homework will be discussed. Two of those exercises will be handed in as summative coursework and the students will be given written feedback on their submissions.
Indicative reading
Lecture notes will be supplied for most topics; otherwise reading from books will be indicated.
Most of the lectures will be based on topics from:
R K Ahuja, T L Maganti and J B Orlin, Network Flows (2013).
Some topics might be from:
David P. Williamson and David B. Shmoys, The Design of Approximation Algorithms (2011).
Assessment
Coursework (10%) in the LT.
Take-home assessment (90%) in the ST.
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Key facts
Department: Mathematics
Total students 2019/20: 35
Average class size 2019/20: 17
Controlled access 2019/20: Yes
Value: Half Unit
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills
