The course will introduce students to a variety of advanced numerical methods including, but not restricted to, numerical linear algebra and the numerical solution of PDEs via finite element, finite difference and spectral methods. Algorithms for problems in optimisation (such as graph theory, linear and nonlinear programming, computational geometry) will be introduced.

2 hours of lectures a week, over 11 weeks.

1 hour tutorial a week over 10 weeks (or equivalent)

5 hours of computer laboratory work

Assessment

60% Final Exam

40% Coursework

Reassessment

In accordance with the University's Code of Assessment reassessments are normally set for all courses which do not contribute to the honours classifications. For non honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students, and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions are listed below in this box.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The course aim is to introduce students to a variety of advanced numerical methods. In the section on Numerical Linear Algebra topics such as

(a) Matrix factorisations

(b) Least squares problems

(c) Eigenvalue problems

(d) Linear systems

will be covered. In the section on Numerical Methods for PDEs, topics such as

(a) Finite element methods (weak formulation, choice of elements and

basis functions, error estimates, examples in 1D & 2D)

(b) Difference methods for parabolic and hyperbolic PDEs (stability and

the CFL condition, upwinding, finite volume methods for hyperbolic

problems)

(c) Spectral methods

will be covered. Other ideas in optimization and computational geometry will also be covered.

On completion of this course students should be able to

1. State the properties of a variety of matrix factorisations and be able to

compute the factorisations for small examples.

2. State and derive methods for the approximation of certain eigenvalues for

A ∈ GLn(R).

3. Formulate methods for solving least squares problems.

4. State the weak form of certain elliptic PDEs and show how to derive the

Finite Element Method.

5. Identify and analyse numerical methods for parabolic equations.

6. State finite difference and flux conservative schemes for the solution of hyperbolic PDEs and analyse their stability.

7. Use appropriate computational software to investigate numerical methods.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.