Computational Optimisation - COMP70007
Aims
In this module you will have the opportunity to:
- learn numerical methods for the solution of non-linear optimisation problems
- apply optimisation in engineering (e.g., the design of energy efficient chemical processes), machine learning (e.g., learning classifiers from data), and finance (e.g., optimal portfolio allocation)
- use analytical techniques and numerical algorithms to solve constrained and unconstrained problems
- identify convexity in a mathematical model and appreciate the importance of convexity both in theory and in practice
- identify necessary and sufficient conditions of optimality for different classes of optimisation models
- be able to apply an appropriate numerical method given the characteristics of the optimisation model
- understand the meaning of Lagrange multipliers
- be able to implement first/second order methods for constrained and unconstrained models
Role
Course Leader
Computational Finance - COMP70006
Aims
In this module you will have the opportunity to be introduced to the fundamental models and mathematical theories to computer science and engineering students. In particular, in this module you will have the opportunity to learn to:
- Understand the time value of money.
- Price derivatives using arbitrage pricing theory
- Optimally design investment strategies that trade-off risk with rewards
- Use efficient numerical methods to solve optimisation models and simulate stochastic processes
Role
Course Leader