# ProfessorRuthMisener

Faculty of EngineeringDepartment of Computing

Professor in Computational Optimisation

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### Contact

+44 (0)20 7594 8315r.misener CV

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### Location

379Huxley BuildingSouth Kensington Campus

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## 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

## 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

## 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

## 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