Dr Giacomo Zambelli, NAB 3.36.

MSc Applicable Mathematics, MSc Management Science (Operational Research), MSc Statistics, MSc Statistics (Research) and as permitted by the regulations.

Students must have a knowledge of linear algebra sufficient to handle matrix inversion and manipulation of partitioned vectors and matrices. Previous experience of computers is not required, but students must be prepared to use computer packages.

To cover the use of mathematical programming models in practice, and an introduction to the theory and computational methods, as described under the headings of the lecture courses below.

• OR406.1 Foundations of Mathematical Programming: An introduction to the mathematical foundations of mathematical programming

• OR406.2 Mathematical Programming: Introduction to theory and the solution of linear and nonlinear programming problems: simplex and interior point algorithms, integer linear programming (ILP) methods (branch and bound, enumeration, cutting planes), decomposition methods, quadratic programming.

• OR406.1 four LT, OR406.1A two x 1.5 LT
• OR406.2 sixteen LT, OR406.2A eight x 1.5 LT, plus revision session

Written answers to set problems will be expected weekly.

V Chvatal, Linear Programming; G Dantzig & M Thapa, Linear Programming 1 and 2; M Padberg, Linear Optimization and Extensions; M Bazaraa, J Jarvis & H Sherali, Linear Programming and Network Flows; J Nocedal & S Wright, Numerical Optimization; S Wright, Primal Dual Interior Point Methods; Nemhauser & Wolsey, Integer and Combinatorial Optimization; A Schrijver, Theory of Linear and Integer Programming; J More & S Wright, Optimization Software Guide; H P Williams, Model Building and Mathematical Programming; H P Williams, Model Solving in Mathematical Programming.

A formal three-hour examination in the ST.