NUMERICAL AND COMPUTATIONAL METHODS - 2022/3
Module code: MAT2001
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice during the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
When an analytical approach is not known or practical for solving a mathematical problem, which is the case for most real-world problems, a numerical approach can be useful in finding approximate solutions that are as close as possible to the exact one. This module introduces a selection of numerical methods for the solution of systems of linear and nonlinear equations, for finding a function that interpolates or approximates a set of data points, for finding numerical values of derivatives and integrals, and for solving initial value problems.
For each numerical method, we will consider the error that results from using approximations and introduce some theories of quantifying the error, which then indicates the accuracy of a numerical solution. We also analyse the complexity of numerical methods in order to estimate the computational resources required for achieving a given accuracy. Students will learn and practice implementing some of the methods in Python.
KLUS Stefan (Maths)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: G130
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 22
Seminar Hours: 6
Laboratory Hours: 5
Captured Content: 22
Prerequisites / Co-requisites
MAT1041, MAT1030, MAT1034
The module will consider the following:
- Algorithms and complexity
- Singular value decomposition
- Systems of linear equations (direct methods)
- Systems of nonlinear equations
- Interpolation and approximation
- Numerical integration
- Numerical solution of ordinary differential equations
- Systems of linear equations (iterative methods)
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAMINATION (2 hours)||80|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
Understanding of and ability to derive, devise and analyse numerical methods.
Subject knowledge through the recall of key definitions, theorems and their proofs.
Analytical ability through the solution of unseen problems in the test and exam.
Practical skills of implementing numerical methods in Python, and ability to understand and interpret given code.
Thus, the summative assessment for this module consists of:
One piece of Assessed Coursework, worth 20% module mark
One final Examination (2 hours), worth 80% of the module mark.
Formative assessment and feedback
Students receive written feedback via a number of marked coursework assignments which include computation projects over an 11 week period. In addition, verbal feedback is provided by lecturer/teaching assistant at lab sessions.
- The aim of this module is to introduce students to a selection of numerical methods in terms of their derivation, their accuracy and efficiency and their implementation.
|001||Demonstrate knowledge and literacy of the taught numerical methods;||K|
|002||For basic numerical methods, prove their convergence and error bounds, and demonstrate understanding of their efficiency;||KC|
|003||Derive and devise numerical schemes with specific details for a range of mathematical problems;||KCT|
|004||Apply the above knowledge to determine the most suitable numerical method(s) for a practical problem;||CPT|
|005||Implement some numerical Linear Algebra methods in Python, and gain proficiency in basic programming structures and methodology that can be used for general coding purposes.||KPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide:
A detailed introduction to the derivation of numerical methods and the concepts of accuracy and efficiency
Experience (through demonstration) of the methods used to find approximate solutions of basic mathematical problems
The learning and teaching methods include:
- 2 x 1 hour lectures per week x 11 weeks with examples to supplement the module lecture notes and Q&A opportunities for students.
- 1 x 1 hour seminar session every 2 weeks (weeks 1, 3, 5, 7, 9, 11) to go through additional examples.
- 1 x 1 hour lab session every 2 weeks (weeks 2, 4, 6, 8, 10) to provide hands-on learning experience of implementing numerical methods in Python. Students are given lab sheets and template codes in order to solve practical problems. Instructor and teaching assistant(s) provide real time guidance, and students can also discuss with peers.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MAT2001
Programmes this module appears in
|Mathematics and Physics BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics and Physics MPhys||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics and Physics MMath||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics MMath||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Statistics MMath||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Statistics BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Music BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.