Computational Optimisation - COMP97055
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 - COMP97026
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
Computational Optimisation - COMP97056
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 - COMP97025
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