## 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

## Operations Research - COMP60016

### Aims

This module will give you the opportunity to:

- explore quantitative mathematical methods for taking decisions in the presence of constraints or finite resources
- learn about linear programming, integer linear programming, robust optimisation, and game theory and their application
- classify mathematical programs on the basis of the number and types of their solutions
- implement solution techniques for linear programs with both real and integer-valued variables
- familiarise yourself with fundamental notions of duality, degeneracy, and sensitivity

### Role

Course Leader

## 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

## Operations Research - COMP96025

### Aims

This module will give you the opportunity to:

- explore quantitative mathematical methods for taking decisions in the presence of constraints or finite resources
- learn about linear programming, integer linear programming, robust optimisation, and game theory and their application
- classify mathematical programs on the basis of the number and types of their solutions
- implement solution techniques for linear programs with both real and integer-valued variables
- familiarise yourself with fundamental notions of duality, degeneracy, and sensitivity

### Role

Course Leader