|| MX31410 |
|| LINEAR ALGEBRA |
|| 2007/2008 |
|| Dr Robert J Douglas |
|| Semester 2 |
|| Dr Robert J Douglas |
|| MA11010 |
|| MA21410 |
| Course delivery
|| Lecture || 19 Hours. (19 x 1 hour lectures) |
|| Seminars / Tutorials || 3 Hours. (3 x 1 hour example classes) |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Exam||2 Hours (written examination) ||100%|
|Supplementary Assessment||2 Hours (written examination) ||100%|
On completion of this module, a student should be able to:
1. determine whether given algebraic structures are vector spaces;
2. apply criteria for subspaces of a vector space;
3. determine bases for vector spaces;
4. prove and apply propositions in the theory of vector spaces;
5. describe the concept of linear transformation;
6. calculate matrices representing linear transformations;
7. determine the rank and nullity of linear transformations and matrices;
8. perform calculations in inner product spaces;
9. diagonalise matrices, especially symmetric matrices, and draw inferences concerning the geometrical context.
In this module the concept of a vector space is introduced. This develops some ideas which have occurred in the first year courses. It will be seen that superficially different problems in mathematics can be unified. For example, the solution of systems of linear equations and linear differential equations are essentially the same process and can be dealt with simultaneously in this context.
To develop some matrix theory techniques which have occurred in the first year courses in an abstract setting. To introduce the concepts of a vector space and a mapping between vector spaces. To develop further techniques for computation in vector spaces and to study some geometrical applications.
1. VECTOR SPACES: Definition and examples, subspaces, spanning sets, linear independence, basis and dimensions.
2. LINEAR TRANSFORMATIONS:Definition and exmples, the matrix of a linear transformation, change of basis. The kernel and image of a linear transformation, rank and nullity. The dimension theorem.
3. INNER PRODUCT SPACES: Definition and examples. Orthogonality and Gram-Schmidt orthogonalisation process.
4. DIAGONALISATION OF MATRICES: Eigenvalues and eigenvectors, characteristic equation. Diagonalisation of matrices.
** Recommended Text
H Anton & C Rorres (2000) Elementary Linear Algebra, the applications version
8th. J Wiley 0471170526
** Supplementary Text
A O Morris (1982) Linear Algebra - An Introduction
2nd. Chapman & Hall 0412381001
D H Griffel Linear Algebra and its applications Vol. 1 & 2
Ellis Horwood 074580571X
Ph Gillett (1975) Introduction to Linear Algebra
Houghton Mifflin Co. 0395185742
R B J T Allenby (1995) Linear Algebra
Edward Arnold 3540610441
S I Grossman (1984) Elementary Linear Algebra
2nd. Wadsworth 0534027385
T A Whitelaw (1983) An Introduction to Linear Algebra
T S Blyth and E F Robertson (1998) Basic Linear Algebra
This module is at CQFW Level 6