MSc Operational Research with Computational Optimization
Why Operational Research with Computational Optimization?
Studying Operational Research with Computational Optimization will give you the opportunity to develop skills in the mathematical theory of methods for optimization and their implementation using techniques of formal programming and high performance computing. You will also learn how to formulate and solve practical problems.
A graduate of this programme would be very attractive to companies who develop their own high performance optimization software and also to firms who are embedding optimization methods into their products. The MSc would also provide an ideal background for PhD studies in this area.
The School of Mathematics at the University of Edinburgh has an exceptionally strong Computational Optimization group. It contains world-class experts in linear, integer, quadratic, nonlinear, convex, nonconvex, global, stochastic, parallel and distributed programming. Group members are especially interested in interior-point methods, parallel simplex methods, advanced integer programming techniques, meta-heuristics, and modern first- and second-order algorithms, with applications ranging from finance, logistics, and manufactring to electricity and oil markets, compressed sensing, and airline ticket pricing.
Structure and course options for the Operational Research with Computational Optimization MSc programme
You will take 120 credits of courses in total during Semesters 1 and 2, followed by a 60 credit dissertation which you complete over the summer. The courses you take will be dependent on the availability of courses each year which may be subject to change as the curriculum develops to reflect a modern degree programme.
Compulsory courses cover the core skills of operational research, with most compulsory courses being studied in Semester 1. All courses are worth 10 credits, unless otherwise indicated.
Semester 1 compulsory courses have previously included:
- Fundamentals of Operational Research
- Fundamentals of Optimization
- Methodology, Modelling and Consulting Skills
- Stochastic Modelling
Semester 2 compulsory courses have previously included:
For your MSc to have a specialization in Computational Optimization, you must also study at least two of the following Semester 2 courses:
- Large Scale Optimization for Data Science
- Topics in Applied Operational Research
- Integer and Combinatorial Optimization
You will have the opportunity to tailor your degree by selecting from a broad range of optional courses. All courses are worth 10 credits, unless otherwise indicated.
Semester 1 optional courses have previously included:
- Statistical Methodology
- Incomplete Data Analysis
- Python Programming
- Statistical Programming
- Introductory Probability and Statistics
- Introduction to Practical Programming with Objects
Semester 2 optional courses have previously included:
- Machine Learning in Python
- Algorithmic Game Theory and its Applications
- The Analysis of Survival Data
- Time Series
- Credit Scoring
- Optimization Methods in Finance
- Generalised Regression Models
- Operational Research in the Energy Industry
The project gives you the opportunity to apply skills developed earlier to real operational research problems. Projects often take the form of a consultancy exercise for a sponsoring organisation. Projects usually involve modelling the problem and applying existing packages and/or developing a computer program for a new application of operational research. It is also possible to have an academic project without a direct link to an external organisation.
Academic projects are defined and supervised inside the department. External projects are defined by some external organisation, which may be an industrial or commercial company, a government body or research lab. There will be one supervisor in the University and one in the outside organisation. In an external organisation, you may work as part of a team on a project, but the work you do must be sufficiently self-contained that it can be written up into a coherent dissertation.
The purpose of the external placement is to enhance your experience through working on a practical project, and to allow you to apply and extend the knowledge and skills that you have already developed as part of your MSc programme. You will also develop your communication and other transferable skills, and interact with others on a more open-ended problem than you will have experienced in the taught part of the MSc.