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

One-hour in-person tutorial three times in the semester (weeks 2, 4, 8)

Two-hour in-person computer labs two times in the semester (weeks 6 and 10)

None

None

Assessment

70% Examination

30% Coursework

It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

This course aims to:

1) develop the ability to mathematically formulate optimisation problems in engineering;

2) advance the knowledge of numerical algorithms for solving optimisation problems;

3) develop an appreciation of the challenges associated with constrained and multi-objective optimisation;

4) establish the tools of variational calculus for deriving Euler-Lagrange equations;

5) advance the ability of analysing the computational complexity of algorithms and compare their energy usage.

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

1) mathematically formulate optimisation problems ;

2) incorporate observable data to inform models;

3) choose appropriate numerical methods to solve a given optimisation problem;

4) explain the meaning of the optimisation and model-fitting results;

5) derive Euler-Lagrange equations;

6) compare different algorithms based on their computational complexity and employ the most appropriate one with least carbon footprint.