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ELEC97111 (EE4-94) Advanced Optimisation

Lecturer(s): Dr Giordano Scarciotti


The aim of this module is to equip the student with the tools needed to formulate and solve applied optimisation problems. The module covers multiple topics including: formulation and solution of applied optimisation problems, convex optimisation, integer programming and heuristics. The topics are covered with an application-driven mindset (i.e. theory and algorithms are covered because they are useful in practice).

The module assumes prior basic optimisation knowledge such as descent methods and constrained optimisation. This prior knowledge may be acquired in the Autumn module "Optimisation". That module is not a formal pre-requisite (i.e. the student is not required to be registered to "Optimisation") but most of the material covered in the module "Optimisation" will be given for granted and used.

Coding is an integral part of the module. Basic knowledge of Python (equivalent to any 4/5-hour long tutorial course available online for free) is required, or it is assumed that the student can pick it up by themselves just before the course starts.

The module is assessed by coursework (25%), individual and group low-stake tests (15%) and exam (60%).

Learning Outcomes

Upon successful completion of this module, you will be able to:
1 - Identify different classes 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 - Evaluate the approximation and computational cost of an optimisation algorithm
5 - Employ heuristics and analyse their limitations
6 - Use a programming language 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.


Approximation and estimation problems, statistical problems, geometric problems, theory of convexity, convex algorithms, use of CVX, integer programmming, mixed-integer problems, branch and cut algorithms, heuristics, genetic algorithms, complex problems from engineering, science and finance.
Exam Duration: N/A
Coursework contribution: 40%

Term: Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

Course Homepage: unavailable

Book List: