Research course

Optimization and Operational Research

University of Edinburgh · School of Mathematics

Entry requirements

A UK first class honours degree, or its international equivalent, in an appropriate subject; or a UK 2:1 honours degree plus a UK masters degree, or their international equivalents; or relevant qualifications and experience.

Months of entry


Course content

As a member of the Operational Research and Optimization research group you will also be part of the Edinburgh Research Group in Optimization (ERGO), a wider association, with our group as the focus, which includes academics at the universities of Dundee and Oxford as well as commercial organisations.

Through its regular seminar series, this network provides interaction with an array of local and international institutions and industrial bodies interested in the development of operational research and optimization. As a result, you’ll establish valuable relationships that will help you take your research to its optimum level.

The School of Mathematics is a vibrant community of more than 60 academic and related staff supervising 60 students.

Our group has as its primary focus the mathematical and computing aspects of optimization. Core technology in optimization is the solution for large sparse linear and quadratic problems, and we’re able to provide world-class expertise in the two main solution methods for these: the simplex method and the interior point method.

We have interests in global optimization, decomposition methods, parallel computing, industrial applications of optimization and stochastic optimization.

Our researchers are currently exploring the following areas:

  • parameter uncertainty in queueing theory and revenue management
  • algorithms for linear and nonlinear nonconvex smooth optimization problems
  • optimization methods for linear, quadratic and nonlinear programming
  • decomposition methods for large-scale nonlinear nonconvex constrained optimization
  • bundle methods
  • warmstarts for interior point methods
  • pooling problems
  • computational techniques for solving large-scale linear programming problems
  • applications of optimization in the chemical, oil and electricity industries
  • efficient gradient methods for large-scale convex and nonconvex optimization problems

Qualification, course duration and attendance options

  • PhD
    part time
    72 months
    • Campus-based learningis available for this qualification
    full time
    36 months
    • Campus-based learningis available for this qualification

Course contact details

Graduate School Administrator
+44 (0)131 650 5085