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Unit information: Optimisation Theory and Applications in 2021/22

Unit name Optimisation Theory and Applications
Unit code EMAT30670
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Colin Campbell
Open unit status Not open

A knowledge of python or MATLAB



School/department Department of Engineering Mathematics
Faculty Faculty of Engineering


This unit gives an overview of methods and algorithms both in linear programming (operational research) and non-linear optimisation. Techniques to be considered include: Linear Programming. Graphical solution of simple problems, basic solutions, the Simplex method, finding an initial basic solution (including artificial variables), multiple optima in the Simplex method, duality. Integer Programming. Some examples of integer programming in finance and engineering. The branch and bound method. Non-Linear Programming. The downhill simplex method. The steepest ascent method. The conjugate gradient method. Dynamic Programming (multi-stage decision making).

Aims: To give students an understanding of theory and engineering applications of linear and nonlinear optimisation, and dynamic programming.

Intended learning outcomes

  1. Students will gain an understanding of a range of techniques in optimisation theory covering linear and non-linear optimisation and dynamical programming.
  2. Through examples students will learn how these techniques can be applied in practice across a diverse range of fields.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, supported by live online sessions, problem sheets and self-directed exercises.

Assessment Details

3 x Summative Coursework Assessments;

Assessed coursework 1 (linear programming ) Weighting 40%
Assessed coursework 2 (nonlinear optimization ) Weighting 40%
Assessed coursework 3 (dynamic programming ) Weighting 20%


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