Course Catalogue

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Optimisation is a common problem faced in engineering, ranging from finding the most efficient solution (e.g., the highest strength structure) to the most economical solution (least cost or carbon emissions). This course covers the mathematical and numerical methods for solving such engineering optimisation problems. The focus is on developing fundamental understanding complemented by practical examples.

2 one-hour in-person lectures every week

1 one-hour in-person tutorial every other week

Mandatory Entry Requirements

None

Recommended Entry Requirements

None

None

None

Assessment

60% Examination

40% Coursework

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

This course aims to develop in students an understanding of the key numerical methods used to solve optimisation problems, including both constrained and multi-objective optimisation, and the applications to which these methods can be applied in engineering. Students will employ this understanding in the solution of practical engineering problems and consider the efficiency of solution techniques.

By the end of this course students will be able to:

• mathematically formulate practical optimisation problems of engineering relevance, and discuss the benefits of optimisation in the development of sustainable and efficient engineering systems;

• compare and evaluate different numerical methods to best solve a given optimisation problem;

• assess optimisation and model-fitting results;

• derive Euler-Lagrange equations;

•compare algorithmic approaches based on their computational complexity and energy usage.

Students must attend the degree examination and submit at least 75% by weight of the other components of the course's summative assessment.

Students should attend at least 75% of the timetabled classes of the course.

Note that these are minimum requirements: good students will achieve far higher participation/submission rates. Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.