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
High Demand Course?	Yes

Course Delivery Information

Academic year 2015/16, 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

Academic year 2015/16, Part-year visiting students only (VV1)		Quota: None
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
No Exam Information

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.