Undergraduate Course: Numerical Linear Algebra and Applications (MATH10059)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Linear Algebra is one of the most widely used topics in the mathematical sciences. Students are taught standard techniques for each of the basic linear algebra tasks at level 8 or 9; including: solving linear systems, eigen-analysis, orthogonalisation of bases, and various factorizations. However, the techniques taught at level 8 and 9 are usually computationally too intensive to be used for large matrices as are typical in applications such as search engines and genomic data sets. NLAA will introduce students to these practical issues, and will present, analyse, and apply algorithms for computing approximate solutions to the above mentioned tasks. |
| Course description |
See 'Course Description' above.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
|
| Academic year 2014/15, Available to all students (SV1)
|
Quota: 54 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
59 )
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 30%, Examination 70% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | MATH10059 Numerical Linear Algebra and Applications | 2:00 | |
Learning Outcomes
1. Understanding of computational cost for algorithms.
2. Understanding of direct methods for solving linear
systems of equations: including the LU and QR
factorizations.
3. Eigen and Singular Value Decompositions, methods for
their calculation, and their applications.
4. Ability to analyse the stability properties of the algorithms
in 2 and 3.
5. Understanding of sparse matrices and how algorithms
should be adapted to exploit sparsity.
6. Applications including image processing, inverse
problems, and search engines.
|
Reading List
| Numerical Linear Algebra and Applications by Biswa Nath Datta |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Study Abroad |
Not Applicable. |
| Keywords | NLAA |
Contacts
| Course organiser | Dr Julian Hall
Tel: (0131 6)50 5075
Email: J.A.J.Hall@ed.ac.uk |
Course secretary | Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
|
© Copyright 2014 The University of Edinburgh - 12 January 2015 4:21 am
|
University Homepage