This course is compulsory on the MSc in Management Science (Operational Research). This course is available on the MSc in Applicable Mathematics. This course is available as an outside option to students on other programmes where regulations permit.

Students must have  a knowledge of Mathematics and Statistics to the level of MA107 (Quantitative Methods - Mathematics) and ST102 (Elementary Statistical Theory).

Mathematical Programming is one of the most powerful and widely-used quantitative techniques for making optimal decisions. The course has a pragmatic focus and aims at enabling students to model and solve real-life management problems. In providing an overview of the most relevant techniques of the field, it teaches a range of approaches to building Mathematical Programming models and shows how to solve them and analyse their solutions. Content includes: An introduction to the theory of linear programming. The modelling life cycle and modelling environments. Formulation of management problems using linear and network models; solution of such problems with a special-purpose programming language; interpretation of the solutions; limitations of such models. Formulation and solution of non-linear models including some or all of binary, integer, convex and stochastic programming models

20 hours of lectures and 18 hours of seminars in the MT. 2 hours of seminars in the LT.

8 hours of computer help sessions in the MT. 2 hours of computer help sessions in the LT. Computer help sessions are optional.

A reading week will take place in W6. There will be no teaching during this week.

Students will have the opportunity to submit a mock project for marking and feedback before the final assessed project is due.  Students will also be given weekly homework exercises.

Central to the course is: H P Williams, Model Building in Mathematical Programming, Wiley. A good introduction to Mathematical Programming provide the relevant chapters of: F S Hillier and G J Liebermann: Introduction to Operations Research, McGraw-Hill. A more theoretical treatment can be found in: D Bertsimas and J N Tsitsiklis: Introduction to Linear Optimization, Athena Scientific. Further suggestions for reading are given during the course.

Project (100%) in the LT.

The course is examined entirely on a project to be submitted at the end of LT.