DR PANOS PARPAS

Faculty of EngineeringDepartment of Computing

//

Contact

+44 (0)20 7594 8366panos.parpas

//

Location

357Huxley BuildingSouth Kensington Campus

//

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

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
• Use efficient numerical methods to solve optimisation models and simulate stochastic processes

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

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