Advanced applied optimisation

Module aims

The aim of this module is to equip you with the tools to formulate and solve applied optimisation problems. The module covers several advanced theoretical topics in optimisation such as convex optimisation and multi-objective optimisation with the aim of formulating and solving applied problems. Each theoretical topic is covered with an application-driven mindset. During the regular classes you will apply the theory on problems arising in a variety of practical domains such as fitting, finance, classification, biology, and advertising.

The module assumes prior basic optimisation knowledge such as descent methods and constrained optimisation. If you are unsure about your background, note that an optional 2-hour self-study pre-course covering the required background is provided (the Autumn module "Optimisation" is not a pre-requisite). Basic knowledge of Python is assumed (self-study resources plus an optional catch-up lab are available on request). More information is available on the course homepage.

Learning outcomes

Upon successful completion of this module, you will be able to:

1. Classify different families of optimisation problems
2. Formulate an engineering/scientific/economic problem as an optimisation problem of a known class
3. Apply the correct methods of optimisation to solve the problem
4. Assess the approximation and computational cost of an optimisation algorithm
5. Employ advanced optimisation techniques and analyse their limitations
6. Write computer programmes to formulate and solve optimisation problems
7. Compare the introduced ideas and tools with the more general theory of optimisation
8. Devise a toolbox of optimisation algorithms to solve various classes of problems.

Module content

Convex sets; Convex functions; Convex optimization problems; Approximation and estimation problems; Statistical applications; Geometric applications; Interior point methods; Integer programming; Methods for solving integer programmes; Multi-objective programming; Pareto optimality; Complexity analysis.

Applied optimisation: classes will focus on problems from different areas of engineering, science and finance.

Module lead

Giordano Scarciotti

ECTS/FHEQ

5/7

Module code

ELEC70066

Host department

Department of Electronic and Electrical Engineering

Term

Spring

Time slot

AM

Teaching weeks

TBC

August resit opportunity?

Yes

How to apply

Please follow the instructions

Application deadline

TBC

Places available (approximate)

TBC

Number of applicants (historic)

TBC

Criteria used for student selection

TBC

Further information

TBC