Course Information Undergraduate prospectus

Optimisation Techniques

Course summary

Course code: MATH1026
Level: 6
Credits: 15
School: Architecture, Computing and Hums
Department: Mathematical Sciences
Course Coordinator(s): Vitaly Strusevich


Pre and co requisites

Operational Research for Industry


To advance students’ knowledge of mathematical modelling in terms of linear or non-linear programming.
To expose them to the theory and solution methods for relevant optimisation problems.

Learning outcomes

On successful completion of this course a student will be able to:

1. create a mathematical model from a practical problem, and to identify the corresponding tool to handle it.
2. use various version of the simplex method of linear programming.
3. solve simple equality and inequality constrained optimisation problems by means of the Lagrange method.
4. interpret the found solution in meaningful terms and perform its analysis with respect to possible changes in the input data.

Indicative content

Linear Programming: the simplex method, duality, the dual simplex method, sensitivity analysis.
Non-Linear Programming: Lagrange multipliers, equality and inequality constraints, Kuhn-Tucker conditions, quadratic programming.

Teaching and learning activity

Concepts are introduced in the lectures and re-inforced in tutorials and by courseworks.
Students are expected to conduct a considerable amount of independent study under the lecturer guidance.


Summative assessment:
Coursework -50%
An individual coursework assessing learning outcomes 1-3.

Exam - 50%
A 3-hour exam assessing all learning outcomes 1-4.

Formative assessment: weekly tutorial exercises