Operational Research with Computational Optimization
Studying Operational Research with Computational Optimization will give students 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. They will also learn how to formulate and solve practical problems.
A student with an Edinburgh MSc in OR with Computational Optimization 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, quadratic, nonlinear, convex, nonconvex, global, stochastic, parallel and distributed programming. Group members are especially interested in interior-point methods, parallel simplex method, modern first- and second-order algorithms, with applications ranging from finance, gene expression analysis, compressed sensing, chemical, electricity and oil markets to airline ticket pricing.
Applications can now be made to study in 2019-20. Note that for entry to this programme, at least one course at university level in probability and statistical theory is essential.
In Semester 1, students must choose a certain number of credits from a given set of themed courses which, previously, has been 20 credits from the following 10-credit courses
- Python Programming
- Object-Oriented Programming with Applications
- Statistical Methodology
- Statistical Programming
- Introductory Probability and Statistics
In Semester 2, students must choose a certain number of credits from given sets of optional courses.