Aims
The aims of the course are to:
Teach some of the basic optimisation methods used to tackle difficult, real-world optimisation problems.
Teach means of assessing the tractability of nonlinear optimisation problems.
Develop an appreciation of practical issues associated with the implementation of optimisation methods.
Provide experience in applying such methods on challenging problems and in assessing and comparing the performance of different algorithms.
Outcome:
As specific objectives, by the end of the course students should be able to:
Understand the basic mathematics underlying linear and convex optimisation.
Be able to write and benchmark simple algorithms to solve a convex optimisation problem.
Understand the technique of Markov-Chain Monte Carlo simulation, and apply it to solve a Travelling Salesman Problem.
Understand the ways in which different heuristic and stochastic optimization methods work and the circumstances in which they are likely to perform well or badly.
Understand the principles of multi-objective optimization and the benefits of such of approaching real-world optimization problems from a multi-objective perspective.
* This module is shared with 4th Year undergraduates from the Department of Engineering.
