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Unit information: Optimisation Theory and Applications in 2021/22

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

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
Pre-requisites

A knowledge of python or MATLAB

Co-requisites

None

School/department School of Engineering Mathematics and Technology
Faculty Faculty of Engineering

Description including Unit Aims

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 Information

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 Information

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%

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EMAT30670).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.

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