2022/23 Undergraduate Module Catalogue

XJCO2421 Numerical Computation

10 creditsClass Size: 100

Module manager: Dr Thomas Ranner
Email: T.Ranner@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2022/23

Pre-requisites

XJCO1421	Fundamental Mathematical concepts
XJCO1721	Object Oriented Programming

This module is not approved as a discovery module

Module summary

Accuracy of floating-point computation. Standard numerical algorithms for linear equation systems, nonlinear equations, ordinary differentialequations and data interpolation. The design of robust and efficient implementations in code.

Objectives

On completion of this module, students should be able to:
- Appreciate the role of numerical computation in computer science;
- Choose a computational algorithm appropriately, accounting for issues of accuracy, reliability and efficiency;
- Understand how to assess/measure the error in a numerical algorithm and be familiar with how such errors are controlled;
- Implement simple numerical algorithms

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-demonstrate a broad understanding of the concepts, information, practical competencies and techniques which are standard features in a
range of aspects of the discipline;
-apply generic and subject specific intellectual qualities to standard situations outside the context in which they were originally studied;
-appreciate and employ the main methods of enquiry in the subject and critically evaluate the appropriateness of different methods of enquiry;
-use a range of techniques to initiate and undertake the analysis of data and information;
-effectively communicate information, arguments and analysis in a variety of forms;

Syllabus

Approximation: converting a real-world problem, via a mathematical model, to a form which can be understood by a computer; discretising a
continuous model; measuring, analysing and controlling approximation errors; balancing accuracy and efficiency. Static systems: simple
iterative methods for solving nonlinear scalar equations; direct and iterative methods for solving linear systems of equations.
Evolving systems: differentiation as rate of change and as the limit of a gradient (including derivatives of simple functions); initial value
ordinary differential equations, simple methods for initial value problems.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	20	1.00	20.00
Tutorial	10	1.00	10.00
Private study hours			70.00
Total Contact hours			30.00
Total hours (100hr per 10 credits)			100.00

Opportunities for Formative Feedback

Coursework and tutorial / lab sessions.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	Coursework 1	20.00
In-course Assessment	Coursework 2	20.00
Total percentage (Assessment Coursework)		40.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc) (S2)	2 hr 00 mins	60.00
Total percentage (Assessment Exams)		60.00

This module is reassessed by Online Time-Limited assessment only.

Reading list

There is no reading list for this module

Last updated: 01/06/2022 16:59:02

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