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MA3NAT-Numerical Analysis II
Module Provider: Mathematics and Statistics
Number of credits: 20 [10 ECTS credits]
Level:6
Terms in which taught: Autumn / Spring term module
Pre-requisites: MA1LA Linear Algebra and MA2NA1 Numerical Analysis I or MA2NAN Numerical Analysis or MT24C Numerical Methods for Environmental Science and MA1RA1 Real Analysis I or MA2RA1 Real Analysis I
Non-modular pre-requisites: Some knowledge of Matlab programming
Co-requisites:
Modules excluded: MA3NA2 Numerical Analysis II
Current from: 2019/0
Email: a.s.lawless@reading.ac.uk
Type of module:
Summary module description:
This course introduces and analyses a range of techniques in numerical approximation, numerical integration, and numerical linear algebra, with connections being made between these areas.Ìý One half of the module will consider the design and analysis of algorithms for the approximate solution of problems of continuous mathematics, with a particular focus on topics such as interpolation, polynomial approximation, and integration.Ìý The other half of the module will introduce and analyse a range of techniques in numerical linear algebra for solving very large systems of linear equations.
Aims:
To motivate, develop and analyse a range of ÌýÌýalgorithms for the approximate solution of problems of continuous mathematics and of techniques in numerical linear algebra.
Assessable learning outcomes:
By the end of the module students are expected to be able to:
- Apply and analyse a range of techniques in approximation theory for appropriate problems;
- Devise, analyse and apply a range of numerical integration techniques;
- Apply and analyse a range of methods in numerical linear algebra including direct and iterative methods;
- Implement a range of numerical methods on a computer.
Additional outcomes:
Outline content:
ÌýIn one half of the module we consider the design and analysis of algorithms for the approximate solution of problems of continuous mathematics, with a particular focus on topics such as interpolation, polynomial approximation, and integration.Ìý The other half of the module will cover stability of linear systems, direct and iterative methods for solving such systems and linear least squares problems.
Brief description of teaching and learning methods:
Lectures supported by problem sheets and tutorials/practicals.
Ìý | Autumn | Spring | Summer |
Lectures | 20 | 20 | |
Practicals classes and workshops | 5 | 5 | |
Guided independent study: | 75 | 75 | |
Ìý | Ìý | Ìý | Ìý |
Total hours by term | 100 | 100 | |
Ìý | Ìý | Ìý | Ìý |
Total hours for module | 200 |
Method | Percentage |
Written exam | 70 |
Set exercise | 30 |
Summative assessment- Examinations:
3 hours.
Summative assessment- Coursework and in-class tests:
A number of assignments and one examination.
Formative assessment methods:
Problem sheets.
Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late:
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.
Assessment requirements for a pass:
A mark of 40% overall.
Reassessment arrangements:
One examination paper of 3 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus coursework marks (70% exam, 30% coursework).
Additional Costs (specified where applicable):
Last updated: 11 April 2019
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.