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Level: MSc
Title: Submodular optimisation
Supervisor:

Dr Justin Ward

Research Area: Combinatorics
Description:

Submodular set functions have many applications in economics, theoretical computer science, and machine learning.  Broadly, these are functions that assign a value to some set of objects in a way that satisfies a natural "diminishing returns" property: the value of any given object may decrease as other objects are selected.  This project will involve reading and understanding some of the fundamental algorithmic results for submodular functions, and then exploring more recent developments in the area. Examples for further directions might include applications in machine learning, evaluating recent algorithms experimentally on large datasets, or studying recent generalisations of submodular functions.
Further Reading:


Key Modules:
Other Information:

Some knowledge of mathematical optimisation, as well as machine learning and/or statistics is helpful, but not required.  Experience with programming would be necessary if you wish to pursue an experimental project.
Current Availability: Yes