Course Details in

Module Title	LH Numerical Linear Algebra with Applications
School	Mathematics
Department	Mathematics
Module Code	06 27712
Module Lead	Dr Chris Good
Level	Honours Level
Credits	10
Semester	Semester 2
Pre-requisites	LI Linear Algebra & Linear Programming - (06 25765) LI Numerical Methods & Programming - (06 25669)
Co-requisites
Restrictions	Students cannot take the LM Numerical Linear Algebra with Application module in combination with this module
Exclusions
Description	Numerical linear algebra is the language of all scientific computing, particularly for applications arising in mathematical and engineering modelling. This module is aimed at applied mathematicians with an interest in numerical methods and more generally scientific computation. After a review of linear algebra topics and an introduction to matrix theory and computation, the module will discuss in detail methods for linear systems of equations, both direct and iterative, methods for eigenvalue problems and an introduction to fast Fourier transforms. The module will include a programming component; in particular, standard algorithmic concepts will be introduced together with notions of computational complexity and good coding practice. Applications drawn from applied mathematics (e.g., dynamical systems, ordinary and partial differential equations etc) will be used for illustration purposes. This module will use the computer package Matlab.
Learning Outcomes	By the end of the module students should be able to: be familiar with a range of common topics from matrix analysis and matrix computations; be aware of the standard methods employed in Numerical Linear Algebra, including methods for linear systems of equations and for eigenvalue calculation; be aware of basic algorithmic concepts and be able to implement and manipulate several linear algebra constructs in a computational environment; be able to formulate several problems of applied mathematics as numerical linear algebra tasks.
Assessment	27712-01 : Raw Module Mark : Coursework (100%)
Assessment Methods & Exceptions	100% from coursework and/or class tests.
Other
Reading List